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Is your digital pathology rollout moving so slowly that it’s creating a fragmented workflow instead of transforming the department?

In this episode of the Digital Pathology Podcast, I speak with Dr. Syed Hoda, Director of Digital Pathology at NYU, about why gradual implementation may no longer be the best approach to digital pathology adoption.

Dr. Hoda explains how NYU used an intensive nine-month planning period to prepare for a department-wide transition. The process involved pathology, IT, project managers, vendors, hospital leadership, and approximately 40–50 people participating in regular planning calls.

This wasn’t simply a scanner installation.

The team mapped workflows, configured Epic Beaker, redesigned laboratory spaces, tested integrations, planned training, and addressed the practical concerns of nearly 100 pathologists.

We also discuss why scanner specifications may matter less than integration, vendor support, training, and system performance. For Dr. Hoda, digital pathology had to work as smoothly as glass microscopy. Speed was non-negotiable.

Change management played an equally important role. Through open discussions, town halls, and the ADKAR framework, the team addressed concerns ranging from ergonomics to the loss of collaborative microscope sessions.

The result? Every pathologist adopted the digital workflow, no one left the department because of the transition, and approximately 60–65 pathologists now work remotely using equipment that matches their office setup.

Finally, we examine the next step: artificial intelligence in pathology. Dr. Hoda explains why NYU focused on building a reliable digital foundation before introducing AI. He also raises important questions about validation, transparency, responsibility, regulatory clearance, and the need for greater pathologist involvement in AI development.

Episode Highlights

• 00:00 — Are we repeating the same mistakes with pathology AI?
  Dr. Hoda compares the current excitement around AI with the early promises made about digital pathology 15 years ago.
• 01:04 — Meet Dr. Syed Hoda
  His clinical pathology background and path to becoming NYU’s Director of Digital Pathology.
• 03:16 — Why going slowly can hold departments back
  How partial adoption creates fragmented workflows, inconsistent training, and prolonged implementation.
• 06:25 — Leadership support for rapid adoption
  Why institutional commitment, resources, and an ambitious timeline made the project possible.
• 10:13 — Nine months of detailed planning
  Workflow mapping, laboratory changes, system configuration, vendor selection, testing, and validation.
• 11:48 — The role of professional project management
  Why pathologists shouldn’t be expected to coordinate every part of a complex digital transformation.
• 14:29 — Why the scanner isn’t the most important decision
  Image quality matters, but integration, service, training, and workflow fit may matter more.
• 17:42 — People matter more than machines
  How vendor relationships and departmental engagement supported adoption.
• 19:19 — Setting clear expectations across the department
  NYU communicated that every pathologist would move to digital sign-out within a defined period.
• 20:49 — Change management is a structured process
  How the ADKAR framework guided communication, education, adoption, and reinforcement.
• 25:07 — Addressing practical and personal concerns
  From mouse ergonomics to preserving collaborative case review between pathologists.
• 27:19 — Why NYU didn’t introduce AI first
  Dr. Hoda explains why pathologists needed to become comfortable with the digital platform before adding new AI tools.
• 29:26 — Digital pathology and remote sign-out
  Approximately 60–65 pathologists now work remotely with equipment matching their office setup.
• 30:28 — Why speed is non-negotiable
  Even a small delay or repeated pixelation can quickly undermine confidence in a digital workflow.
• 33:25 — A cautious approach to pathology AI
  Concerns about premature adoption, self-validation, limited regulatory clearance, and lack of pathologist involvement.
• 37:27 — Scientific validation, transparency, and responsibility
  What happens when the AI result and the pathologist’s interpretation don’t agree?
• 40:41 — Where AI could meaningfully augment pathology
  Quantifying microenvironments, feature combinations, ratios, and findings that are difficult to assess visually.

Resources Mentioned

• ADKAR change management framework
• Digital Pathology Association
• Executive War College
• FDA list of AI-powered medical devices
• A radiology mock-trial paper examining responsibility when clinicians use AI: Examining perceptions of liability about AI in radiology (MedRxiv)
• Why AI cannot do good science without humans (Nature Editorial)
• A previous Digital Pathology Podcast discussion about AI-supported colorectal cancer feature analysis (How to use deep learning image analysis for colon cancer with Rish Pai)

Listen to the full conversation for a practical look at digital pathology planning, change management, remote sign-out, scanner integration, and responsible AI adoption.

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If AI is already being used across the drug development pipeline, why hasn’t its impact matched the investment?

AI can help researchers review scientific literature, predict protein structures, prioritize molecules, assess toxicity, support clinical trials, and monitor adverse events. But access to better tools doesn’t automatically create better drugs.

In this episode, I speak with Thibault Geoui, Science CDO and host of the Tech & Drugs Podcast, about where AI is making a practical difference in drug discovery and development—and where the results remain limited.

We map AI across the full drug development funnel, from basic research and target identification to preclinical testing, clinical trials, regulatory documentation, commercialization, and pharmacovigilance.

We also discuss why digital-native tech-bio companies may be better positioned to benefit from AI than traditional pharmaceutical organizations. The difference isn’t simply the model. It’s how data, people, laboratory experiments, and AI tools are connected inside the workflow.

For digital pathology professionals, the conversation becomes especially relevant when we examine AI-powered biomarker development, the role of pathology in pharmaceutical research, and the Roche–PathAI case discussed in the episode.

And, of course, we talk about the problem every AI user eventually faces: an answer can look polished, specific, and completely convincing—and still be wrong.

Episode Highlights

00:00 — When convincing AI output creates more work
Why AI can accelerate information generation while increasing the time required for review and verification.

02:15 — From structural biology to science and technology leadership
Thibault shares his background in X-ray crystallography, structural biology, scientific data, and digital product development.

15:36 — Understanding the drug discovery and development funnel
How thousands of potential compounds are narrowed down through discovery, preclinical research, clinical trials, and approval.

20:00 — AI for scientific literature review
How alerts, filtering, summarization, and information extraction can help researchers manage a rapidly growing scientific literature base.

22:32 — AlphaFold and protein structure prediction
What faster access to predicted protein structures changes for researchers—and why structural prediction alone doesn’t solve drug discovery.

24:13 — Searching an enormous chemical space
How AI can help design and prioritize potential molecules for synthesis and experimental testing.

25:50 — Predicting efficacy and toxicity
Where AI supports preclinical research, why the models remain imperfect, and why experimental validation still matters.

29:38 — Has AI changed drug development outcomes yet?
A practical discussion about drug approval rates, AI investment, uneven returns, and the difference between deploying a tool and integrating it into a process.

34:33 — Why traditional pharma struggles to scale AI
Siloed data, legacy systems, organizational complexity, and the need to build reusable data workflows.

37:57 — The “lab in the loop” model
How tech-bio companies connect AI predictions with wet-lab experiments and feed the new data back into their models.

44:37 — Can tech-bio companies shorten development timelines?
How digital-native organizations are changing parts of the discovery and preclinical process.

58:00 — AI, pharma, and digital pathology
What the Roche–PathAI case discussed in the episode may indicate about the role of pathology data, biomarker discovery, and pharmaceutical workflows.

01:06:17 — AI errors in regulated environments
Why responsibility remains with the person or company submitting AI-assisted work, regardless of which tool produced it.

01:17:37 — The growing cost of AI tools
Subscriptions, token limits, model selection, AI orchestrators, and the need to use expensive tools more intentionally.

01:27:50 — What successful AI adoption requires
Starting with focused pilots, training scientists and technologists together, and treating implementation as organizational change.

01:30:26 — The AI quirks that still frustrate users
Hallucinated information, ignored writing instructions, stylistic habits, and poor awareness of time and context.

The episode’s timestamped themes and examples are documented in the supplied summary. The broader discussion covers AI from literature mining and molecular design through clinical development and post-market monitoring.

Resources Mentioned

• Thibault Geoui’s LinkedIn profile
• Tech & Drugs Podcast
• MIT NANDA study on generative AI implementation and return on investment
• Insilico Medicine as an example of a digital-native tech-bio company

AI is already changing how scientific work gets done. The bigger question is whether organizations can redesign their workflows, train their teams, and maintain the human oversight needed to use it well.

Listen to the full episode for a practical look at AI in drug discovery, drug development, and digital pathology.

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What does AI literacy actually look like for pathologists, researchers, and future clinicians? And how do you teach it in a way that is practical, not abstract?

In this episode, I talk with Candice Chu, DVM, PhD about something I think a lot of people in digital pathology and computational pathology are feeling right now: AI is moving fast, but education is still catching up.

Candice is a clinical pathologist, veterinarian, and educator building AI-focused teaching and research at Texas A&M. We worked together before on digital pathology and image analysis projects, so this conversation felt especially grounded. We talk about her AI literacy curriculum framework for veterinary education, why she decided to build it, and what it takes to teach AI in a way that is useful, ethical, and realistic.

This episode is about understanding what AI tools are good for, where they can waste your time, and why hands-on experience matters. Candice explains why she sees AI as a set of tools, not a belief system. Try them. Learn them. Keep what improves your workflow. Drop what does not.

We also talk about the difference between putting educational content online and building formal institutional teaching. That matters because social media can move quickly, but curriculum changes, research, and professional organizations shape longer-term adoption. Candice shares how her course started as a low-stakes elective, then grew into a more structured framework that combines education with publishable research.

A big part of this conversation is the curriculum itself. We go through what students actually learn: AI fundamentals without heavy math, machine learning and image analysis, large language models, prompt engineering, chatbot building, ethics, literature research, and final projects where students evaluate real tools and workflows. I liked that the course does not stop at theory. It asks students to use tools, question them, and explain where they help and where they do not.

We also get into something that matters far beyond veterinary medicine: professional responsibility. If AI is involved in a workflow, the clinician is still responsible. That includes fabricated citations, bad outputs, weak prompts, and the temptation to trust tools too quickly. Candice makes a strong case that AI education needs ethics, legal context, and interdisciplinary teaching built in from the start.

If you are trying to think more clearly about AI in pathology, education, workflow design, or professional training, this episode gives you a concrete example of what responsible AI literacy can look like.

Episode Highlights

00:00 – Why AI tools are just tools, and why trying them matters even if you later decide not to keep using them

00:33 – Who Candice Chu is and why her work on AI literacy in veterinary medicine is worth paying attention to

02:33 – Why going back to Texas A&M changed the scale of Candice’s AI research and teaching

07:53 – How the AI course was designed as a low-stakes elective first, and why that helped student engagement

11:16 – Where veterinary AI education stands now, and what professional organizations like ACVP are doing

13:08 – Why AI adoption in veterinary medicine is still slow, and what skepticism usually sounds like in practice

15:19 – Real examples of how Candice uses LLMs and computer vision in pathology, medical records, and research

19:58 – What is actually inside the 15-week AI literacy curriculum, from fundamentals to final projects

24:16 – Why ethics and legal responsibility are not optional in AI education

31:35 – Why no-code tools and vibe coding are entering the curriculum already

38:50 – The AI tools Candice is testing in her own workflow, including Claude, Codex, and Perplexity

Resources mentioned

• Candice Chu’s AI literacy curriculum framework paper in Frontiers in Veterinary Science
• Candice’s earlier work on ChatGPT in veterinary medicine
• Texas A&M and the institutional setting where Candice is building AI research and teaching
• Mr. Don Riddick and the AVMA AI working group, mentioned in the ethics and legal context
• Claude, Codex, and Perplexity as AI tools Candice is actively testing
• Digital Pathology 101, mentioned in the conversation as a teaching resource
• Candice’s online educational work on Instagram.

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Are pathology foundation models actually ready for labs, or are they still stronger on paper than in practice?

In this episode of DigiPath Digest #49, I unpack a timely review on pathology foundation models and ask the question that matters most to me: not just what these models can do, but what has to be true before they are genuinely useful in real pathology workflows.

I walk through how pathology AI moved from narrow, task-specific models into the era of transformer-based foundation models. That shift matters because pathology is no longer only about looking at H&E in isolation. Today, pathologists are expected to integrate morphology, immunohistochemistry, molecular assays, genomics, and clinical context. That growing complexity is one reason foundation models are getting so much attention.

In this discussion, I explain how transformers entered pathology, why image patches are treated like tokens, and how shared embeddings can support classification, regression, segmentation, and multimodal retrieval. I also go through the major pathology foundation models mentioned in the paper, including Virchow/Virchow2, Mayo Clinic Atlas, UNI, CONCH, H-Optimus, GigaPath, and TITAN, and why scale alone is not the full story.

A big part of this episode is about the gap between benchmark performance and clinical readiness. I talk about the persistent limitations in training data diversity, the overuse of TCGA, and why public benchmarks can still miss what real pathology practice looks like. I also cover where foundation models still struggle, especially in cytopathology, hematopathology, and underrepresented disease areas, along with the real-world problems of artifacts, domain shift, concept drift, infrastructure burden, regulatory complexity, and workflow disruption.

For me, one of the most important themes is this: AI in pathology should augment, not replace, pathologists. The future is not about handing diagnosis to a model. It is about building tools that support pathologists better, fit real workflows, and can be validated in ways that deserve trust.

I also spend time on what comes next: explainable AI, counterfactual explanations, conversational interfaces, retrieval-augmented systems, multimodal fusion, and the need for deployment-centric validation rather than paper-only excitement.

If you are trying to understand where pathology foundation models really stand today, this episode will help you separate the promise from the practical barriers.

Episode Highlights

00:01 – Why I chose this paper, what is changing at Digital Pathology Place, and why foundation models are worth paying attention to now.

02:15 – The core questions: what pathology foundation models are, where they are, and how difficult they are to apply in pathology.

04:50 – Why pathology is becoming more cognitively demanding, and how multimodal complexity is driving interest in scalable AI.

07:02 – From narrow AI to transformers: how pathology moved beyond single-task CNN models.

10:16 – How transformers work in pathology: image patches as tokens, self-attention, embeddings, and downstream tasks.

14:16 – Why multimodality matters, and what kinds of data foundation models may eventually integrate.

15:27 – Timeline of key model developments, from “Attention Is All You Need” to gigapixel-scale pathology foundation models.

17:13 – The leading models and what scale really looks like: Virchow, Mayo Clinic Atlas, UNI, CONCH, H-Optimus, and GigaPath.

19:51 – Why dataset diversity matters more than sheer volume, and why TCGA is not enough.

23:17 – Where foundation models still struggle: cytopathology, hematopathology, rare disease, artifacts, scanner shifts, and pen marks.

28:06 – Explainability, counterfactual explanations, and why trust in pathology AI needs more than attention maps.

30:17 – The real deployment hurdles: regulation, infrastructure, workflow fit, and economics.

36:32 – Why AI should augment pathologists, not replace them, and which tedious tasks pathologists would gladly hand over.

38:36 – Retrieval-augmented and conversational AI in pathology: where interactive systems may actually help.

40:51 – Vision-language models and multimodal fusion with histology, radiology, genomics, and clinical notes.

42:16 – The path forward: deployment-centric design, prospective multi-site validation, and human-AI collaboration.

44:08 – Closing thoughts on AI literacy, community learning, and what needs to happen next.

Resources Mentioned

• Main paper discussed:
  Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspective
  https://doi.org/10.3390/bioengineering13050577
• Review article / journal landing page:
  https://doi.org/10.3390/bioengineering13050577
• Benchmarks mentioned:
  • PathoBench — discussed in the review paper; use the review link here for context until you want to swap in a canonical project page:
    https://doi.org/10.3390/bioengineering13050577
  • PathBench — public benchmark paper:
    https://arxiv.org/abs/2505.20202
  • MEDFAIR — benchmark paper:
    https://arxiv.org/abs/2210.01725
  • MEDFAIR code repository:
    https://github.com/ys-zong/MEDFAIR
• Models mentioned:
  • Model overview in the review (Virchow/Virchow2, UNI, CONCH, H-Optimus, GigaPath, TITAN, Mayo Clinic Atlas):
    https://doi.org/10.3390/bioengineering13050577
  • Virchow:
    https://arxiv.org/abs/2309.07778
  • UNI:
    https://arxiv.org/abs/2308.15474
  • CONCH:
    https://arxiv.org/abs/2307.12914
  • Mayo Clinic Atlas:
    https://arxiv.org/abs/2501.05409
  • TITAN:
    https://arxiv.org/abs/2411.19666
• Dataset mentioned:
  The Cancer Genome Atlas (TCGA)
  https://portal.gdc.cancer.gov/
• Book mentioned:
  Digital Pathology 101: All You Need to Know to Start and Continue Your Digital Pathology Journey
  https://digitalpathologyplace.com/
• Platform:
  Digital Pathology Place
  https://digitalpathologyplace.com/

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How far can pathologists take visual biomarker scoring before human vision becomes the bottleneck?

In this episode, I talk with Doug Bowman. PhD, VP Precision Medicine at Indica Labs, about what happens when companion diagnostics move from traditional visual scoring into the era of AI-powered image analysis. Doug comes from a biomedical and electrical engineering background, with experience in microscopy, digital image analysis, pharma workflows, and now precision medicine at Indica Labs. That combination makes him a great person to talk to about how image analysis actually fits into real companion diagnostic development.

We start with a very practical question: what is a companion diagnostic, and why is it becoming so important in precision medicine? Doug explains that companion diagnostics are developed alongside therapeutics to help identify which patients are most likely to benefit from a specific treatment, especially in more complex therapies like antibody-drug conjugates (ADCs). We use HER2 as an example, and from there we get into the real challenge: once a biomarker cutoff matters clinically, visual estimation around that cutoff becomes much harder than many people want to admit.

That is where this conversation gets especially useful for pathologists and digital pathology trailblazers. We talk about the limits of human vision, why low or ultra-low biomarker expression is difficult to score consistently, and how AI helps at multiple levels of the workflow: slide QC, tissue classification, cell segmentation, membrane and cytoplasmic measurement, and spatial analysis. Doug makes the case that AI is not only a convenience here. In some cases, it is the only realistic way to capture the kind of quantitative information modern therapies need.

We also get into one of the more interesting examples from the episode: the Trop2 story, where a ratio of cytoplasmic to membrane expression appears to predict therapeutic efficacy better than looking at one compartment alone. That kind of compartment-level quantitation is exactly where computational pathology becomes more than a digital version of what the eye already does. It starts uncovering measurements and signatures the eye cannot reliably extract on its own.

Another important part of the discussion is workflow and regulation. Doug walks through how AI-powered companion diagnostics are developed from preclinical work, to human feasibility studies, to RUO or clinical trial assays, and eventually toward analytical and clinical validation with regulatory engagement happening early. We also talk about the Indica Labs and Leica Biosystems partnership, and why end-to-end capability matters when you are trying to build something clinically deployable rather than just analytically interesting.

What I liked about this conversation is that it stayed grounded. We did not talk about AI as magic. We talked about image analysis as a method, companion diagnostics as a workflow, and precision medicine as something that only works when the measurement is good enough to support real decisions.

Episode Highlights

00:00 – Why AI matters in slide QC, tissue classification, and cell-level analysis before you even get to the biomarker score.

00:54 – Doug Bowman’s background in biomedical engineering, microscopy, and digital image analysis.

05:16 – What a companion diagnostic actually is, and why it is critical for targeted therapies and ADCs.

07:34 – Why visual biomarker scoring becomes unreliable around critical cutoffs, especially in low-expression cases.

10:09 – How AI expands the workflow: slide QC, tissue classification, and precise cell segmentation.

13:07 – Why pathologists remain central in AI workflows through validation, markup review, and model refinement.

16:31 – The Trop2 example: when cytoplasmic-to-membrane ratio tells you more than one compartment alone.

20:23 – The Indica Labs + Leica Biosystems partnership and why end-to-end workflow matters in companion diagnostics.

22:53 – What the development journey looks like from early algorithm work to RUO, validation, and regulatory interaction.

26:51 – Multiplexing, spatial analysis, and why more clinical value often comes with more deployment complexity.

33:29 – Why image analysis literacy matters, and how shared language between pathologists and scientists becomes essential.

40:13 – Where to learn more about Indica Labs and who to contact for collaboration.

Resources mentioned

• Indica Labs
• Indica Labs contact – info@indicalab.com
• HALO software / HALO AI diagnostic image analysis – discussed in the context of companion diagnostic deployment and pharma services.
• Leica Biosystems GT450DX – referenced as an FDA-cleared slide scanner in the Indica-Leica partnership.
• Digital Pathology Association – mentioned as part of the broader educational ecosystem for digital pathology and image analysis.
• Digital Pathology Place / Digital Pathology Podcast – the platform hosting this conversation and related education around digital pathology and AI.

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Can AI copilots really keep up with pathologists when the cases are new, the workflow is messy, and the benchmark is actually protected from leakage?

In this episode of DigiPath Digest #48, I focus on one paper: DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset. I chose this paper because I think the field needs more of this kind of work. Less hype. More evaluation. Less “look what AI can do.” More “how do we test it in a way that actually means something?”

In this session, I look at what makes DALPHIN important for pathologists, lab leaders, and digital pathology trailblazers trying to make sense of pathology AI right now. The paper benchmarks three models against human pathologists: two general-purpose models, Gemini 2.5 Pro and GPT-5, and one pathology-specific model, PathChat+. The dataset includes 1,236 images from 300 cases, covering 130 diagnoses, 14 pathology subspecialties, and cases from six countries. Human performance is benchmarked with 31 pathologists from 10 countries.

What I like about this paper is that it does not stop at top-line performance. It deals with the benchmarking problem itself. The authors built a sequestered, indirectly accessible ground truth so the evaluation data could not simply be scraped into model training. That matters because without that protection, benchmarking can become an illusion of genius rather than a real test of generalization.

The results are interesting and more nuanced than a simple win-or-lose story. PathChat+ reached expert-level performance in four of six tasks, Gemini in two of six, and GPT in one of six. That tells us something important already: pathology-specific training matters. But it also does not mean pathology is solved. In organ recognition, expert pathologists still outperformed all the models. In rare cancers, none of the models reached expert-level performance. And in ambiguous cases, the models still struggled with something human pathologists do all the time: expressing uncertainty.

I also spend time on one of the most practical parts of the paper: model behavior. Gemini tended to overcall. GPT tended to undercall. PathChat was more balanced. That matters in practice. A pathologist using a copilot needs to know the tool’s calibration bias before they can safely interpret what it is telling them. I also talk about anchoring bias in conversational interfaces, where early hallucinations can propagate through later answers if memory is not reset between questions. That is not just a technical curiosity. That is a workflow and safety issue.

Why should you listen? Because this episode is really about a bigger question: What kind of evidence should pathologists demand before AI copilots enter real workflows? If you want to understand validation, data leakage, rare-case performance, uncertainty, and why these tools should still be treated as co-pilots rather than autopilots, this is a useful paper to know.

Episode Highlights

01:20 – Why I chose the DALPHIN preprint and why benchmarking matters right now.

05:38 – What is in the DALPHIN dataset: 300 cases, 130 diagnoses, 14 subspecialties, 6 countries.

07:57 – Top-line performance: PathChat+ reaches expert-level performance in 4 of 6 tasks.

09:41 – The benchmarking trap of data leakage and why DALPHIN’s sequestered ground truth matters.

12:19 – Why real pathology diagnosis is not text-only and why macro + micro context matters.

15:26 – Tissue recognition, neoplasm detection, ambiguity, and conversational memory: how the testing was structured.

21:29 – The diagnostic personalities of the models: overcalling, undercalling, and balanced behavior.

24:36 – Rare cancers: where AI copilots still fall short of expert human performance.

28:00 – Why binary outputs are not enough when pathology often lives in uncertainty.

31:37 – Anchoring bias and conversational memory: how early hallucinations can keep propagating.

37:11 – Why these tools should be treated as co-pilots, not autopilots.

40:29 – Resources for beginners: Digital Pathology 101 and continued AI literacy.

Resources mentioned

• DALPHIN preprint: arXiv:2605.03544v1
• DALPHIN evaluation platform: dalphin.grand-challenge.org
• PathChat+ pathology-specific AI model discussed in the benchmark.
• Digital Pathology 101 free eBook by Dr. Aleksandra Zuraw.
• Educational streams on tissue recognition and computer vision literacy mentioned in the session.

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Do you really need a scanner, whole slide images, and AI infrastructure before you can start in digital pathology?

In this episode, I argue that you do not.

I’m Dr. Aleksandra Zuraw, veterinary pathologist and digital pathology educator, and this talk is about a belief I hear all the time: I don’t have the tools yet, so there is no point learning digital pathology. I used to think that too. When I was training in Berlin, there was one Leica 6-slide scanner, and it felt like digital pathology was only for a small group of chosen people. That experience made the field feel distant, exclusive, and not really available to beginners.

What changed for me was not a new scanner. It was a small project.

I needed a more consistent way to quantify a senescence marker in archived skin samples, so I used a microscope camera, captured images, opened them in Microsoft Paint, and manually marked cells with colored dots. It was scrappy. Very low tech. But it was also digital, consistent, and verifiable. That project became my first real step into digital pathology and helped me get my first job in the field, where I worked between pathologists and image analysis scientists on biomarker quantification and patient stratification problems.

That is the core point of this episode: knowledge unlocks technology.

Scanners matter. AI tools matter. But the deeper bottleneck is whether enough people understand how to use these tools, ask good questions, and connect pathology expertise with digital workflows. That is why this episode is really about readiness. Not readiness of the hardware. Readiness of the people.

I also talk about Dr. Taladzer from Pakistan, whose story makes this point even more clearly. At the time, Pakistan had around 220 million people, about 500 pathologists, and zero scanners. She still started learning digital pathology during COVID using a microscope and camera, joined the Digital Pathology Association, taught herself from papers and online resources, and kept going even after multiple AI vendors rejected her because she did not have whole slide images. Eventually, she found a DIY image analysis platform, learned to annotate and train models on static images, completed projects quickly, and went on to publish more than 10 digital pathology papers without ever using WSI.

Why should you listen?

Because this episode is for pathologists and lab leaders who are interested in digital pathology but still feel stuck at the beginning. It is for people waiting for permission, perfect infrastructure, or a formal roadmap. And it is for trailblazers who came back from a meeting or conference energized, but need a practical way to turn that energy into action before it fades.

I also address an important AI question near the end: How do we know an AI model is good enough for pathology? I talk about why models are only as good as the pathologist annotations used to train them, why concordance between pathologists matters, how orthogonal labels like IHC can improve model quality, and why pathologists still need to stay in the loop as these systems develop and get deployed.

If you are trying to figure out where to start, this episode gives you a practical answer: start where you are. Start with what you have. Start learning now.

Episode Highlights

00:00 – Why the real barrier to digital pathology is usually not the hardware
00:33 – What it feels like to be at the beginning of the digital pathology journey
02:50 – My first practical digital pathology project using a microscope camera and Microsoft Paint
05:37 – How that low-tech project led to my first digital pathology job
08:52 – Why knowledge, not infrastructure, is the real unlock
09:57 – Dr. Taladzer’s story: starting digital pathology in Pakistan with zero scanners
12:03 – What happened after repeated vendor rejection and why persistence mattered
14:39 – The “forgetting loop” vs the “commitment loop” after conferences
16:48 – Practical next steps: book, PubMed alerts, journal clubs, webinars, vendor resources
18:52 – Why I believe digital pathology is the gateway to faster diagnosis
20:00 – How to think about whether an AI model is really ready for pathology

Resources Mentioned

• Digital Pathology 101 – free book recommended as a starting point for learning digital pathology.
• Digital Pathology Association – mentioned as a learning resource and professional community.
• PubMed alerts for AI and digital pathology.
• Journal clubs – mentioned as one way to keep learning consistently.
• Webinars and vendor resources – suggested as practical ways to keep building knowledge.
• A4A – the DIY image analysis platform that supported Dr. Taladzer’s early work with static image annotation and model training.

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Why are pathology vendors still speaking different image languages when radiology solved that problem decades ago?

In this episode of DigiPath Digest #46, I talk through four papers that all point to a bigger issue in digital pathology: we are not only dealing with better algorithms. We are dealing with interoperability, workflow design, explainability, and whether the field is actually ready to use these tools well.

I start with DICOM in digital pathology, because I think this is still one of the most important infrastructure questions in the field. Digital pathology has clear value for consultation, image analysis, archival, and workflow, but vendor-specific whole slide image formats still create silos. In the episode, I explain why DICOM matters, why adoption is still low, how the multi-resolution pyramid works, and why this is really about enterprise imaging and future-proofing, not just file conversion.

Then I move into kidney transplant rejection, where the paper makes a strong case for multimodal precision diagnostics. Creatinine is late. Antibody testing can miss important biology. Biopsies can miss the area that matters. So the opportunity is not to replace pathology, but to combine biomarkers, biopsy, and machine learning in a way that is more useful than any one signal alone. I also talk about explainability here, because if a model gives a risk score, we need to know what contributed to it.

The third paper focuses on perineural invasion in solid tumors, and I liked this one a lot because it shows how AI can help standardize something that is clinically important but still inconsistently detected and reported. Perineural invasion is not just a passive pathway of spread. The biology is more active than that, and the quantification can go far beyond a simple yes-or-no answer. This is a good example of where digital pathology can do something humans cannot realistically do by eye at scale.

The last paper is on gastric cancer immunohistochemistry biomarkers and advanced quantification, including HER2, PD-L1, mismatch repair, and CLDN18.2. This section is really about complexity. We are now asking pathologists to visually score biology that is getting harder and harder to summarize consistently, especially when markers, spatial context, and multiplexing all start to matter at once. I make the case that computational pathology is becoming necessary here, not because pathologists are failing, but because the biology is outgrowing purely visual workflows.

What ties these four papers together is simple: digital pathology is not only about remote reading anymore. It is about interoperability, quantification, explainable AI, and making pathology more precise in places where the old workflow is reaching its limit. If you are a pathologist, lab leader, or digital pathology trailblazer trying to figure out what actually matters right now, this episode will help you connect the dots.

Episode Highlights

07:41 – Why DICOM still matters if we want digital pathology systems to work together.
14:39 – Current adoption of SVS, MRXS, and DICOM, and why DICOM is still lagging.
16:44 – How the DICOM whole slide image pyramid works and why it matters for workflow.
24:29 – Why kidney transplant rejection is still difficult to diagnose with any single marker.
29:18 – Why perineural invasion is clinically important and still inconsistently reported.
34:44 – How AI can quantify tumor-nerve relationships more consistently than visual review alone.
46:39 – Why gastric cancer biomarker scoring is getting too complex for purely visual workflows.
54:55 – Multiplexing, spatial biology, and why explainable AI matters in biomarker interpretation.
01:04:01 – What is really blocking digital pathology adoption: cost, workflow, regulation, or mindset?

Resources mentioned

• DICOM / digital pathology interoperability paper
  https://pubmed.ncbi.nlm.nih.gov/42093730/
• Kidney transplant rejection, biomarkers, and artificial intelligence
  https://pubmed.ncbi.nlm.nih.gov/42073482/
• Perineural invasion in solid tumors with AI and machine learning applications
  https://pubmed.ncbi.nlm.nih.gov/42100436/
• Gastric cancer IHC biomarkers, advanced detection methods, and perspectives
  https://pubmed.ncbi.nlm.nih.gov/42075555/
• Digital Pathology Place
  https://digitalpathologyplace.com
• Digital Pathology 101
  Free PDF book mentioned at the end of the episode through Digital Pathology Place.

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What if the most frightening part of a pathology report is not the word cancer, but the silence that follows?

In this episode of the Digital Pathology Podcast, Dr. Aleksandra Zuraw talks with Michele Mitchell—breast cancer survivor, caregiver, national patient advocate, and longtime volunteer across Michigan Medicine, ASCP, the Digital Pathology Association, and MyPathologyReport.ca—about what happened when she saw her own cancer slide years after treatment. That moment changed how she understood her disease, her risk, and her role as a patient advocate.

This is not just a patient story. It is a digital pathology implementation story.

The episode looks at how digital pathology removes practical barriers to sharing slides, why pathology clinics matter, and what becomes possible when pathologists move from being hidden in the background to becoming direct contributors to patient understanding. Michelle and Dr. Aleks talk through the communication gap around pathology reports, the emotional cost of delayed explanation, and the real-world workflow of pathology clinic visits built to help patients review their slides with the pathologist who made the diagnosis.

They also discuss what the 21st Century Cures Act changed for patients, why immediate access to reports without interpretation can still create fear, and how pathology clinics can bridge the gap between raw data and real understanding. The conversation gets practical too: how patients can request a pathology clinic visit, what virtual pathology consults can look like, how billing and workflow concerns are already being addressed, and why the infrastructure question is smaller than many people assume.

If you work in digital pathology, pathology informatics, patient communication, or implementation, this episode is a reminder that visibility is not extra. It is part of the value proposition. And for pathologists who worry this is too far outside the traditional role, the episode offers a grounded counterpoint: the workflows, templates, billing structures, and virtual options already exist.

Highlights

• 00:00 – Why pathology needs to become more patient-centered
  Michele frames the core problem clearly: what often scares patients is not only cancer, but the silence around the diagnosis.
• 00:34 – How digital pathology changes the patient experience
  Digital slides make it possible for patients to see their diagnosis, compare normal and abnormal tissue, and ask better questions.
• 11:13 – What happened when Michele saw her cancer for the first time
  More than a decade after treatment, seeing her own slide changed how she understood her grade, her risk, and her daily health decisions.
• 16:19 – Why visual pathology can change adherence and lifestyle
  Michele explains how the image-based explanation became a practical turning point, not just an emotional one.
• 20:43 – The case for direct pathologist-patient communication
  The episode reviews why this can improve clarity, treatment understanding, clinic efficiency, and even professional satisfaction for pathologists.
• 38:40 – What a pathology clinic actually looks like
  From preparation and consent to slide review, plain language, empathy, and follow-up, the workflow is much more concrete than many people assume.
• 45:35 – ASCP’s certification workshop for pathology clinics
  Michele describes the national effort to make pathology clinics reproducible, scalable, and easier to implement.
• 49:32 – What the 21st Century Cures Act changed
  Patients now get near real-time access to reports, but that access still needs interpretation, context, and support.
• 01:03:23 – Pushback, logistics, and why the barriers are not where people think
  Time, reimbursement, scheduling, and virtual setup are addressed directly with examples already in practice.
• 01:16:57 – The future: patient-friendly reports, AI, and pathology as part of the care team
  The episode closes on a practical vision: not hype, but tools and workflows that already exist and can be connected now.

Resources mentioned

• Digital Pathology Place – website and educational platform referenced by Dr. Aleks as the home for her work and resources.
• Digital Pathology 101 – Dr. Aleks’s book, referenced in the broader discussion of patient and pathologist education.
• Michigan Medicine breast pathology clinic – launched in 2023 as a patient-facing breast pathology clinic model.
• ASCP pathology clinic certification workshop – national workshop co-developed to help institutions build pathology clinics.
• 21st Century Cures Act – legal framework behind near real-time patient access to pathology reports and related health data.
• MyPathologyReport.ca – patient-friendly pathology education resource reviewed with patient advocate involvement.
• American Cancer Society Reach to Recovery – support resource mentioned for breast cancer patients.
• Scanslated – patient-friendly report interface discussed as part of a future-facing model for pathology communication.
• Virtual pathology consults/telehealth setup – discussed as a scalable way to lower implementation friction.

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DigiPath Digest #45 asks a practical question: can AI in pathology move from correlation to real clinical use? In this episode, I review four papers that push on that question from different angles: computational pathology moving toward morphology-driven molecular inference, the current state of digital cytopathology and AI, multi-omics and precision oncology in hepatocellular carcinoma, and AI literacy in veterinary education. What ties them together is not model performance alone. It is the harder question of validation, workflow fit, quantitative use, ethics, and human oversight.

In the first paper, I talk about computational pathology as more than pattern recognition. The focus is on morphology-driven molecular inference, digital biomarkers, and why spatial omics matters as biological ground truth. I also discuss why continuous quantitative scoring is more useful than forcing biology into rough scoring buckets.

The second paper focuses on digital cytopathology. Cytology was early for FDA-cleared AI in cervical screening, but non-gynecologic cytology is still much harder to digitize because of specimen variability and workflow complexity. I also cover telecytology, rapid onsite evaluation, automation, and quality control.

The third paper looks at hepatocellular carcinoma and AI-driven precision oncology. This part is about using AI and machine learning to integrate genomics, transcriptomics, proteomics, metabolomics, radiomics, and pathology to support biomarker discovery, tumor microenvironment analysis, and treatment stratification.

The fourth paper may be the most broadly useful. It proposes an AI literacy curriculum for veterinary education that covers AI fundamentals, machine learning evaluation, LLMs, ethics, liability, and academic integrity. I think that matters far beyond veterinary medicine, because if clinicians are expected to use AI tools responsibly, AI literacy cannot stay optional.

Highlights
00:01 Welcome and overview of the four papers
03:02 Computational pathology and morphology-driven molecular inference
11:01 Digital cytopathology, telecytology, and QC
20:47 AI/ML in hepatocellular carcinoma precision oncology
31:04 AI literacy in veterinary education
47:42 Final takeaways and Digital Pathology 101 update

Resources

1. Computational Pathology as a Mechanistic Discipline: From Morphology to Molecular Data
   https://pubmed.ncbi.nlm.nih.gov/42052846/
2. Advances in Digital Cytopathology and Artificial Intelligence Applications
   https://pubmed.ncbi.nlm.nih.gov/42046894/
3. Navigating the Labyrinth of Hepatocellular Carcinoma: Leveraging AI/ML for Precision Oncology
   https://pubmed.ncbi.nlm.nih.gov/42065059/
4. Curriculum Framework for Artificial Intelligence Literacy in Veterinary Education
   Front Vet Sci. 2026;13:1801756

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Where is AI in pathology actually becoming useful right now? In this episode of DigiPath Digest, I review 4 new PubMed papers across digital pathology, whole slide imaging (WSI), computational pathology, medical education, forensic pathology, and breast cancer AI. We look at a deep learning tool for coronary artery stenosis measurement in forensic autopsies, an AI-powered digital pathology model for renal pathology education, an open-source quality control tool for prostate biopsy whole slide images, and a breast cancer stage prediction model built for resource-constrained settings using low-magnification H&E slides. I also share updates on the upcoming second edition of Digital Pathology 101 and the decision to make AI paper summaries public on the podcast feed to help busy pathology professionals stay current.

Highlights

[01:28] Update on the upcoming second edition of Digital Pathology 101 and the release of public AI paper summaries for faster literature review.

[05:22] Paper 1: Deep learning for coronary artery stenosis evaluation in forensic autopsies using whole slide imaging. Why objective stenosis measurement matters, how the model outperformed visual estimates, and why this could affect adoption in forensic pathology.

[15:18] Paper 2: AI-powered digital pathology with case-based teaching in renal education. A practical discussion on annotated digital slides, flipped classroom learning, and how digital pathology can improve pathology education and diagnostic reasoning.

[21:34] Paper 3: Open-source AI for quantitative quality control in prostate biopsy whole slide images. Why WSI quality control matters, what PathProfiler measures, and how automated QC can support remote pathology workflows.

[32:38] Paper 4: Breast cancer stage prediction from H&E whole slide images in resource-constrained settings. A look at low-magnification AI, vision transformers, and what moderate performance can still mean when access to advanced testing is limited.

[45:06] Closing thoughts, invitation to vote for future AI paper summaries, and a final reminder to download Digital Pathology 101.

Resources
Paper 1: Development of a deep learning-based tool for coronary artery stenosis evaluation in forensic autopsies using whole slide imaging
PubMed: https://pubmed.ncbi.nlm.nih.gov/41998396/

Paper 2: Integrating AI-Powered Digital Pathology With Case-Based Teaching: A Novel Paradigm for Renal Education in Medical School
PubMed: https://pubmed.ncbi.nlm.nih.gov/41995002/

Paper 3: Application of an open-source AI tool for quantitative quality control in whole slide images of prostate needle core biopsies - a retrospective study
PubMed: https://pubmed.ncbi.nlm.nih.gov/41994924/

Paper 4: Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings
PubMed: https://pubmed.ncbi.nlm.nih.gov/41993946/

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Paper Discussed in this Episode:

Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings. Bedőházi Z, Biricz A, Kilim O, et al. Journal of Pathology Informatics 21 (2026) 100644.

Episode Summary:

Welcome back, Trailblazers! In this Journal Club deep dive of the Digital Pathology Podcast, we flip the core assumption of microscopic precision on its head. Can an AI accurately predict pathological breast cancer stages (pTNM I-III) from a blurry, high-altitude 2.5x magnification snapshot? We explore a 2026 study that strips away standard high-resolution data to build a highly efficient, resource-aware AI diagnostic tool for clinics lacking supercomputers. We unpack the math, the models, and a haunting revelation about what primary tumors can tell us about distant metastasis.

In This Episode, We Cover:

• The Compute Bottleneck: Why the digital pathology AI revolution is leaving resource-constrained clinics behind, and how dropping from the standard 40x to 2.5x magnification slashes image patch extraction by 256 times, bypassing massive hardware and server requirements.

• The "Airplane View": How the AI compensates for the loss of microscopic cellular details (like mitosis or cellular atypia) by relying on macroscopic features, identifying disease through overall tumor growth patterns and broad architectural disruption.

• Vision Transformers & "Puzzle Bags": Why the UNI foundation model—a vision transformer fine-tuned on the BRACS dataset—outperforms older convolutional networks (like ResNet-50) by mapping long-range spatial dependencies across the entire image patch simultaneously. Plus, how Multiple Instance Learning (MIL) acts as a targeted "puzzle bag," mathematically weighting critical cancer data and ignoring irrelevant background noise.

• The Real-World Stress Test: The model's solid performance on the internal Semmelweis dataset versus the massive external Nightingale cohort, where unsupervised data cleaning with t-SNE and DBSCAN clustering automatically deleted garbage data. We also discuss the AI's struggle with the TCGA-BRCA dataset due to severe domain shift from heterogeneous tissue preparation, specifically the structural tissue damage caused by frozen sections.

• The "Messy Middle" and Clinical Triage: The model's tendency to struggle with Stage II breast cancer and the critical clinical danger of under-staging advanced Stage III cancers. We discuss why this WSI-only baseline isn't replacing human pathologists, but rather serves as an automated "sorting hat" for incomplete medical records or a highly tunable "smoke detector" to route suspicious slides for immediate manual review.

Key Takeaway:

The AI successfully predicted overall cancer stage—which inherently includes distant lymph node metastasis—by looking only at the primary tumor's architectural disruption, without ever evaluating a single lymph node slide. This proves that vital systemic biological secrets are hiding in plain sight in the macroscopic view of standard H&E slides, offering a phenomenal proof-of-concept for global health equity in resource-constrained settings

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Paper Discussed in this Episode:

Integrating AI-Powered Digital Pathology With Case-Based Teaching: A Novel Paradigm for Renal Education in Medical School. Zhou H, Cui L. Clin Teach 2026; 23(3):e70421. doi: 10.1111/tct.70421.

Episode Summary: In this journal club episode tailored for healthcare trailblazers, we explore a massive paradigm shift in medical education. We examine a 2026 perspective article that uses the notoriously complex field of renal pathology as a stress test for a brand-new teaching model. Moving away from dark lecture halls and static, perfect images, we discuss what happens when artificial intelligence is actively combined with flipped classrooms, fundamentally redefining what it means to be a competent physician in the digital age.

In This Episode, We Cover:

• The "Bottleneck" of Renal Pathology: Why the kidney is the ultimate teaching hurdle. Students must translate the dense, flattened 2D reality of an H&E stain into an understanding of a patient's complex systemic autoimmune response.

• The Danger of the "Curated Reality": Why traditional teaching methods that rely on textbook-perfect, heavily curated slides create "brittle" mental models. When students finally encounter messy, real-world biopsies with overlapping, ambiguous pathologies, the traditional educational foundation falls apart.

• The "Spell Checker" for Histopathology: How collaborative AI elevates Whole Slide Imaging (WSI) beyond just high-resolution screens. The AI acts as a concurrent guide, using pixel-level pattern recognition to highlight regions of interest simultaneously and simulate the complex reasoning process of an expert pathologist.

• The Case-Based Flipped Classroom (CBFC): The pedagogical engine that anchors these AI tools in clinical reality. Instead of passive lectures, students are handed the "detective's case file" beforehand to actively interrogate annotated slides, synthesizing diverse data streams to defend diagnoses in collaborative groups.

• Redefining Medical Competence (The "Clinical Editor"): Why the new bottleneck in medical education isn't memorization—it's critical appraisal. We discuss the necessity of teaching "digital literacy," training students to skeptically manage AI, recognize its blind spots (like confusing a physical tissue fold for an abnormality), and actively audit the algorithm against the messy human reality of the patient.

• The Impending Culture Collision: A look at the fascinating future where freshly minted, AI-native residents enter a legacy clinical workforce still transitioning away from physical glass slides, potentially reversing traditional medical hierarchies in the hospital.

Key Takeaway: The goal of modern medical education is no longer just memorizing histological patterns, as that heavy lifting is being outsourced to algorithms. By fusing AI-powered digital pathology with the necessary friction of case-based learning, we are training a new generation of diagnosticians to view AI not as a crutch, but as a powerful collaborative tool that must be thoughtfully scrutinized and audited for safe patient care

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Paper Discussed in this Episode:

Spatial omics and AI for clinically actionable cancer biomarkers. Reitsam NG. PLoS Med 2026; 23(4): e1005049.

Episode Summary: In this deep dive, we explore how artificial intelligence and spatial omics are fundamentally rewriting the rules of cancer diagnostics. We break down a 2026 editorial that challenges a deceptively simple question driving modern oncology: Is a tumor "positive" or "negative" for a biomarker? As targeted cancer therapies evolve, this binary thinking is failing us. We discuss why mapping where and how much of a therapeutic target exists is crucial, and how AI is stepping in to solve the reproducibility issues human pathologists face when making borderline diagnostic calls.

In This Episode, We Cover:

• The Illusion of "Positive" vs. "Negative": Why the basic premise of modern cancer therapies—like antibody-drug conjugates (ADCs)—often falls apart in reality when we ignore the spatial heterogeneity of a tumor.

• The Power of Computational Pathology: How AI is transforming subjective, qualitative estimates into continuous, reproducible data, scaling the quantification of complex biomarkers like PD-L1 and TROP2.

• "Virtual" Proteomics: The fascinating concept of using AI models to infer high-dimensional spatial information and immune maps directly from standard, routine H&E stained slides.

• The HER2 Bottleneck: A real-world look at the breast cancer drug T-DXd, which now demands pathologists distinguish between "HER2-low" and "HER2-ultralow". While human agreement drops below 70% at these fuzzy decision boundaries, AI steps up with a staggering ~97% sensitivity.

• Three Shifts for the Future: Why clinical trials and routines must adopt continuous measures (like percentage of expressing cells), demand longitudinal repeat testing at disease progression, and utilize adaptive trial platforms.

• Bridging the Gap to Reality: The massive hurdles preventing widespread adoption—such as equipment costs exceeding $250,000 and massive data storage needs. We discuss why a hybrid workflow that bolsters routine pathology with deployable AI is the best path forward to prevent widening global health disparities.

Key Takeaway: The future of precision oncology isn't just about finding new drug targets; it’s about fundamentally changing how we measure them. By moving away from rigid binary thresholds and using AI to map the continuous, spatial reality of tumors, we can unlock the true potential of targeted therapies. However, achieving this diagnostic ecosystem requires overcoming significant financial and systemic hurdles—such as updating reimbursement pathways and proficiency testing—to ensure these life-saving insights are accessible across all healthcare settings.

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Paper Discussed in this Episode: Advancements in bone marrow biopsy: the role of omics and artificial intelligence in hematologic diagnostics. Maryam Alwahaibi and Nasar Alwahaibi. Front. Med. 2026; 13:1772478.

Episode Summary: In this journal club deep dive, we explore a paradigm shift in hematopathology, moving from 19th-century visual assessments to the cutting edge of precision medicine. We examine a 2026 review that unpacks how combining artificial intelligence with multi-omics technologies is transforming the traditional bone marrow biopsy from a static, subjective snapshot into a live, interactive, predictive 3D map. We ask: What happens when deep learning can predict underlying genetic mutations just by analyzing the visual shape and texture of a cell?.

In This Episode, We Cover:

The Breaking Point of Traditional Diagnostics: Why the 150-year-old gold standard of H&E staining and human visual assessment is hitting a biological and operational wall, plagued by subjectivity, high variability, and observer fatigue.

The Multi-Omics Multiverse: Moving beyond standard genomics to unpack the complex biological machinery of the marrow, including:
Epigenomics: The biological "switches," like DNA methylation, that control cell fate and can kick off malignant transformation without altering the underlying DNA sequence.
Lipidomics: How cellular fats form specialized signaling rafts that actively remodel the marrow's communication network.
Microbiomics (The Gut-Marrow Axis): How systemic inflammation driven by gut dysbiosis acts like a massive "traffic jam" that indirectly disrupts local bone marrow homeostasis and blood cell production.

AI as the Ultimate Analytical Partner: How artificial intelligence serves as a bridge between physical tissue morphology and high-dimensional molecular data. We discuss AI tools like MarrowQuant for objective cellularity mapping and the Continuous Index of Fibrosis (CIF) that replaces clunky human guesswork with a granular, predictive metric.

Predicting Genotype from Phenotype: The revolutionary capability of deep learning models to predict underlying genetic mutations (like TET2 or del 5q MDS) purely from the subvisual, spatial arrangement and shape of cells on a standard slide.

Roadblocks and Solutions: Why this technology isn't universally adopted yet. We break down the "black box" problem of AI, the brittleness of algorithms in different clinical settings, and how innovations like Federated Learning and Explainable AI (using heat maps) are overcoming these hurdles.

Key Takeaway: The integration of AI and multi-omics is redefining our understanding of bone marrow diseases. By uncovering invisible molecular machinery and objectively translating it through transparent algorithms, we are moving away from subjective human bottlenecks toward a highly personalized, predictive model of hematologic care.

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Paper Discussed in this Episode: Artificial intelligence in clinical oncology: Multimodal integration and translational development. Ruichong Lin, Zhenhui Zhao, Zhonghai Liu, Jin Kang, Kang Zhang, Xiaoying Huang, Yunfang Yu. Cancer Letters 2026; Volume 649, 218493.

Episode Summary: In this journal club deep dive, we explore how cutting-edge AI is fundamentally rewriting the rules of cancer diagnostics. We examine a comprehensive 2026 review on clinical oncology that highlights the shift from narrow, single-modality algorithms to highly sophisticated multimodal AI. We discuss how machines are learning to cross-reference patient charts, genomic data, and medical imaging simultaneously to achieve unprecedented feats—like accurately predicting tumor mutations without ever performing a physical biopsy. Plus, we explore the controversial but necessary world of "computational hallucinations" or synthetic data, which is currently being used to solve diagnostic blind spots.

In This Episode, We Cover:

• The Fragmentation Bottleneck: Why keeping radiology, pathology, genomics, and clinical history in isolated silos limits our ability to treat the whole patient, and why single-modality AI suffers from severe diagnostic "tunnel vision".

• Cross-Modal Attention & Non-Invasive Biopsies: How models like LUCID essentially mimic the deductive reasoning of a multidisciplinary tumor board. By utilizing cross-modal attention mechanisms, LUCID dynamically shifts focus between CT scans, routine labs, and text-based clinical charts to predict EGFR gene mutations in lung cancer entirely non-invasively.

• Graph Neural Networks (GNNs) & Tumor Social Networks: A look at the NePSTA framework, which uses GNNs and spatial transcriptomics to treat the tumor microenvironment like a mathematical topology. By mapping the "social network" of cells, it can rapidly molecularly subtype notoriously ambiguous central nervous system (CNS) tumors in minutes.

• Computational Hallucinations: Introducing MINIM, a generative AI foundation model that creates statistically valid, photorealistic synthetic medical images (like optical CT or chest X-rays) for rare diseases based on textual descriptions. We discuss how intentionally generating these synthesized images solves the critical "data scarcity" problem and directly improves real-world diagnostic accuracy.

• The Reality Check - Distribution Shifts: The dangerous logistical reason why an AI model boasting near-perfect accuracy at a massive urban academic center might fail completely in a rural clinic due to differing scanner calibrations and population demographics. We emphasize why the field must transition away from retrospective "vanity metrics" and toward clinically trustworthy prospective validation.

• The Virtual Cell Paradigm: A staggering look into the near future where AI constructs completely accurate, computationally interactive digital twins of a patient's cancer. This framework allows doctors to test different drug regimens and simulate cellular responses mathematically in silico before ever administering medicine to the actual patient.

Key Takeaway: Multimodal AI proves that cancer diagnostics must go beyond isolated data points. By dynamically synthesizing highly fragmented clinical information and utilizing synthetic imaging to overcome rare disease data scarcity, AI is pushing oncology into an era of robust, individualized molecular phenotyping. Ultimately, these innovations are replacing risky, invasive testing with precision computational predictions

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I did something I've never done before for this episode — I went live from the middle of a national park. This is DigiPath Digest #42, broadcasting from the Great Sand Dunes National Park in Colorado via Starlink from my family road trip. Yes, it actually worked. And so did the papers.

This episode covers four papers that all ask the same uncomfortable question from different angles: how close is AI to being genuinely useful in real pathology practice — and what's still standing in the way? From LLMs interpreting cervical Pap smears, to AI guiding breast cancer treatment decisions from a simple H&E slide, to a practical roadmap for bringing generative AI into oncology workflows — this one covers a lot of ground.

I also introduced something new: my AI-powered paper summary podcast subscription. For $7 a month, AI hosts summarize digital pathology literature in a journal-club style so you can stay current without spending hours reading abstracts. I walk through how it works and why I built it.

What we cover:

• [00:00] Going live from the wilderness — Starlink, sand dunes, and a very cold morning
• [02:01] How I use AI-generated audio summaries to prep for each DigiPath Digest
• [03:19] Paper 1: Can LLMs like ChatGPT and Gemini interpret cervical cytology? Spoiler: ~47–48% exact concordance — promising, but not there yet
• [10:23] Bonus: My new AI-powered paper summary subscription — $7/month, journal-club style
• [14:05] Paper 2: AI in oral oncology — CNNs for early lesion detection, multimodal prognostics, and the real barriers still blocking clinical adoption
• [20:28] Paper 3: Generative AI in oncology — from chat tools to agentic EHR-integrated assistants, and why augmentation is the goal, not automation
• [25:35] Paper 4: Computational pathology in breast cancer — predicting BRCA1/2, HER2, Oncotype DX, and treatment response from standard H&E slides
• [31:39] Final thought: the floor just got raised for all of us — how I think about new technology in pathology

Resources & Links:

• Paper 1 – LLMs & Cervical Cytology (PubMed): https://pubmed.ncbi.nlm.nih.gov/41931983/
• Paper 2 – AI in Oral Oncology (PubMed): https://pubmed.ncbi.nlm.nih.gov/41930554/
• Paper 3 – Generative AI in Oncology Practice (PubMed): https://pubmed.ncbi.nlm.nih.gov/41930309/
• Paper 4 – AI & Digital Pathology in Breast Cancer (PubMed): https://pubmed.ncbi.nlm.nih.gov/41930306/
• Watch on YouTube: https://www.youtube.com/live/O2hOU4gM0Bk?si=oH8iJ8HiBb29USG3
• Digital Pathology Place: https://www.digitalpathologyplace.com

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Paper Discussed in this Episode:
How to bring generative AI to oncology practice. D. Truhn & J. N. Kather. ESMO Real World Data and Digital Oncology 2026.
Episode Summary:
In this journal club deep dive, we step out of the theoretical sci-fi hype of artificial intelligence and look at a practical, real-world roadmap for bringing Generative AI into oncology. We examine a 2026 paper that maps out the trajectory for deploying Large Language Models (LLMs) to combat the overwhelming cognitive load of modern cancer care. Rather than replacing clinicians, this episode explores how AI can synthesize massive amounts of unstructured data—like dense pathology narratives and shifting molecular reports—so doctors can get back to practicing medicine instead of acting as data entry clerks.
In This Episode, We Cover:
• The Data Avalanche in Oncology: Why the shifting landscape of decades of patient histories, clinical trial registries, and handwritten notes creates an information load that human cognition simply wasn't evolved to process all at once.
• How LLMs Actually "Think": Why predicting the "next word" based on massive training data allows AI to mimic medical reasoning and organize complex clinical concepts—like linking a BRAF mutation directly to a specific inhibitor without looking up a rulebook.
• The Three Evolutionary Steps of AI Complexity: ◦ Step 1: Stand-alone Models: The "closed-book exam." These models (like early ChatGPT) are frozen in time with their original training data and have zero access to new clinical trials or FDA updates. ◦ Step 2: Retrieval-Augmented Generation (RAG): The "open-book exam." The AI searches continually updated external databases and guidelines before answering, significantly reducing fabricated answers, or "hallucinations". ◦ Step 3: Agentic AI: The ultimate goal. Fully functioning "research assistants" that can iteratively reason, plan steps, and invoke external software tools (like lab APIs and medical calculators) to complete complex tasks like proposing tumor board summaries.
• The Deployment Roadblocks: Why you can't just drop an autonomous agent into a fragmented hospital IT network built in 2005. We unpack strict security silos, audit logs, and the dangerous reality of "domain shift"—where an AI trained perfectly at Johns Hopkins might silently fail at a community clinic simply due to different doctor shorthand or microscopic slide scanner colors.
• The Human Element & Automation Bias: The hidden dangers of junior doctors losing their clinical intuition (deskilling) and why system design must force the AI to "show its work" with intentional friction to prevent doctors from blindly clicking accept on a hallucinated treatment plan.
• Your Edits Are the Future: A fascinating look at how a clinician's daily administrative annoyances—every strike-through and manual correction of an AI draft—serve as the ultimate, high-value ground-truth data to train the next generation of oncology AI.
Key Takeaway:
The destination we are driving toward is augmentation, not automation. By handling massive information synthesis, uncovering patterns, and explicitly showing its work, AI can act as a tireless assistant that improves routine care, while leaving the final, nuanced clinical judgment exactly where it belongs: with the human physician.

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Paper Discussed in this Episode: Can large language models like ChatGPT and Gemini interpret cervical cytology accurately? Saroja Devi Geetha. Annals of Diagnostic Pathology 2026; Volume 83, 152641.

Episode Summary: In this journal club deep dive, we explore what happens when advanced artificial intelligence is thrown into the visually chaotic realm of human biology. We examine a 2026 study evaluating whether two massive multimodal models—GPT-5 and Gemini 2.5 Pro—can accurately read digital cervical Pap smears without any prior fine-tuning,,. We unpack how these general-purpose models perform on highly specialized visual tasks, revealing that while they aren't ready to fly solo, they exhibit fascinating and distinct diagnostic "personalities" that will undoubtedly reshape the future of the pathology lab,.

In This Episode, We Cover:

• The "Textbook" Test Setup: How researchers tested the baseline visual reasoning of GPT-5 and Gemini 2.5 Pro by feeding them 100 curated, gold-standard digital Pap test images from the Hologic Education Site to classify using the Bethesda System,,.

• The Clinical Reality Check: While the models only achieved a coin-toss exact diagnostic match rate (47% for GPT-5 and 48% for Gemini), their accuracy jumped to 66% when evaluating clinical management protocols—proving they are beginning to grasp the underlying severity and medical consequences of cellular abnormalities,,.

• The Over-Anxious Resident (Gemini 2.5 Pro): Gemini acted like a highly sensitive but unrefined trainee, hitting 84% sensitivity and expertly spotting infectious organisms (71%),,. However, its tendency to confuse dense, overlapping cellular clumps with high-grade squamous intraepithelial lesions (HSIL) led to massive overcalling, dragging its specificity down to 71% and creating a risk of false alarms,.

• The Big-Picture Academic (GPT-5): GPT-5 proved to be much more measured, demonstrating better overall specificity (74%) and excelling at identifying subtle structural shifts like low-grade squamous intraepithelial lesions (LSIL) (75%) and glandular changes,. Yet, in its focus on the big picture, it completely missed obvious infectious organisms, scoring a dismal 20%,.

• The Future of the Lab - Prompt Engineering & The Algorithmic Auditor: Why the next era of cytopathology requires rigorous AI fine-tuning on proprietary datasets and cytology-specific prompt optimization. We discuss a major paradigm shift where human pathologists may transition from actively hunting for disease to acting as "algorithmic auditors" whose primary job is to filter out the hyper-vigilant machine's noise,.

Key Takeaway: Current multimodal LLMs are not yet reliable for independent Pap test interpretation due to critical blind spots and tendencies to overcall lesions,. However, their out-of-the-box performance establishes a staggering baseline. By understanding their unique mechanical flaws, pathologists can prepare to use these systems as highly effective co-pilots, seamlessly combining the algorithm's computational brute force with the indispensable filter of human medical reasoning

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Paper Discussed in this Episode: Artificial intelligence in oral oncology: Current advances and future potential in diagnosis, prognosis, and therapeutic decision-making. Annamalai A, Dhanes V, Jayalakshmi L, Shanmugam R, Ravi S. Cancer Treatment and Research Communications 47 (2026) 101193.

Episode Summary: In this journal club deep dive, we explore how AI is fundamentally reshaping the clinical management of Oral Squamous Cell Carcinoma (OSCC). We examine a comprehensive March 2026 study that confronts a frustrating paradox: despite the oral cavity being visible to the naked eye, OSCC survival rates have stagnated due to late-stage diagnosis and complex tumor biology. This episode breaks down how algorithms are moving oncology from a reactive discipline to a highly predictive, personalized science.

In This Episode, We Cover:

• The OSCC Paradox: Why relying on traditional visual inspection and standard TNM staging ignores biological heterogeneity, and how AI steps in where the naked eye and basic anatomy fall short.

• Pocket Pathologists: The revolutionary use of Convolutional Neural Networks (CNNs) in smartphone apps and portable devices, achieving up to 82% to 92% sensitivity for point-of-care screening in resource-constrained settings.

• The Committee of Algorithms: How AI acts as a "multimodal synthesizer," fusing radiomics (tumor texture), histopathology (tumor-infiltrating lymphocytes), genomics, and Natural Language Processing (NLP) of unstructured clinical notes to predict individualized risk.

• Real-Time Margin Guidance: How AI combined with fluorescent imaging provides surgical margin feedback to surgeons in the operating room in under five minutes with over 85% concordance with expert histopathologists.

• Digital Twins: The sci-fi reality of running virtual clinical trials. We discuss how AI uses reinforcement learning to build simulated patient copies, allowing tumor boards to predict radiotherapy outcomes and drug toxicities before treating the physical person.

• The Black Box, Bias, and the Fix: The major roadblocks preventing immediate clinical rollout. We discuss opaque decision-making and training data bias (which can drop accuracy by over 15% in underrepresented groups). We also explore the solutions: Explainable AI (Grad-CAM heat maps) to visualize decision logic, and Federated Learning (privacy-preserving decentralized training) to eliminate data sharing hurdles.

Key Takeaway: The true value of AI in oral oncology isn't in replacing human clinicians, but in digesting massive multi-omics data that no single human could synthesize alone. By acting as a transparent, explainable support tool, AI is setting the stage for a future where tomorrow's healthcare professional might spend as much time treating a virtual patient as the physical one sitting in the chair

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Paper Discussed in this Episode: How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer. T. Ruelle, T. Grinda, L. Del Mastro, M. Lacroix-Triki, B. Pistilli & G. Gessain. ESMO Real World Data and Digital Oncology 2026.

Episode Summary: In this journal club episode, we step into the reality of computational pathology and explore how artificial intelligence is fundamentally transforming breast cancer diagnostics. We examine a comprehensive review detailing how AI not only assists overburdened healthcare systems but also unlocks invisible genomic data straight from a standard $5 hematoxylin-eosin (H&E) glass slide. What happens when a machine can predict complex DNA mutations just by evaluating the structural architecture of cells?

In This Episode, We Cover:

• The Diagnostic Bottleneck: Understanding the critical worldwide shortage of pathologists colliding with a projected 3.2 million global breast cancer diagnoses by 2050, and why the system is under unprecedented strain.

• The Biomarker Battle: Why the human visual cortex struggles to quantify faint immunohistochemistry stains, and how AI acts as a perfect "digital colorimeter". We discuss its near-perfect concordance in assessing crucial biomarkers like Ki-67, ER, PR, PD-L1, and the newly established HER2-low status.

• Seeing the Invisible (Predictive AI): How deep learning transcends visual diagnostics to predict treatment outcomes, such as a patient's response to neoadjuvant chemotherapy. We also discuss AI's ability to infer Homologous Recombination Deficiency (HRD) and BRCA1/2 mutations by identifying macroscopic footprints like laminated fibrosis.

• Decoding Genomic Assays: The potential to replace expensive, tissue-consuming genomic tests like Oncotype DX with AI models (such as Orpheus) that predict recurrence risk straight from digitized slides, achieving accuracy that rivals the tests themselves.

• Roadblocks to Reality: The major clinical friction preventing global rollout. We discuss the steep infrastructure costs of whole-slide scanners, the danger of AI bias across diverse hospital datasets, and the ethical "black box" problem requiring the evolution of transparent, agent-based AI.

Key Takeaway: Computational pathology is moving far beyond basic diagnostic assistance. By successfully reading the structural language of biology, AI proves it can extract costly, invisible molecular data from standard biopsies, fundamentally changing the economics and accessibility of global personalized healthcare

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You don't need a fancy scanner, a huge budget, or a computational background to get started in digital pathology. That's what I told the ACVP podcast — and I meant it. In this episode, I share my full digital pathology journey: from being completely intimidated by scanners during residency, to building a career that combines toxicologic pathology, image analysis, and remote work at a global CRO.

If you're a resident, a trainee, or even a seasoned pathologist who hasn't fully stepped into the digital space yet — this one's for you.

We talked about practical ways to get started, what foundation models actually mean for our daily work, how to build a team when implementing digital pathology at your institution, and why change management might be the most underestimated skill in this whole process.

What we cover:

• [00:00] My background — from veterinary school in Poland to digital pathology
• [03:22] Why I chose industry over academia, and what that transition looked like
• [05:02] How a simple IHC side project became my entry point into digital pathology
• [07:11] How digital slides helped me pass my boards — and fall back in love with histopathology
• [10:24] My first job at a digital pathology image analysis company
• [12:00] What my current role at Charles River Laboratories looks like day-to-day
• [13:53] The best free resources for trainees to start exploring digital slides RIGHT NOW
• [15:26] Why pathologists need to understand image analysis principles — segmentation, classification, object detection
• [19:31] Foundation models, transformer architecture, and why annotation bottlenecks may soon be a thing of the past
• [24:13] Practical advice for institutions implementing digital pathology — equipment, teams, and managing resistance to change
• [27:30] How I unplug: trail running, weight training, and pathology-themed earrings

Resources & Links:

• Joint Pathology Center (JPC) digital slides: https://www.jpc.org
• Davis Thompson Foundation — Noah Slidebox: https://www.davisthomasonfoundation.org
• QuPath (free, open-source image analysis): https://qupath.github.io
• Digital Pathology Place: https://www.digitalpathologyplace.com
• Watch the full conversation on YouTube: https://youtu.be/wTDdlxJzq-A?si=xkz5YNljrUX5Snhd

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How close is pathology AI to making decisions that matter in real workflows, real trials, and real patient care?

In this episode of DigiPath Digest, I review five recent papers that approach that question from very different angles. We look at multimodal survival prediction in cervical cancer, pathology-driven response assessment in neoadjuvant immunotherapy for head and neck squamous cell carcinoma, AI-assisted Ki-67 scoring in pulmonary neuroendocrine neoplasms, automation and AI in hematologic diagnostics, and AI-based qFibrosis readouts from the Phase 3 MAESTRO-NASH trial.

What I liked about this set of papers is that they do not all tell the same story. Some show clear progress. Some show where AI already works well as an adjunct. Others make it very clear that validation, governance, reproducibility, and workflow design still matter just as much as model performance.

Key topics and timestamps

• 00:00 Introduction, Easter edition, and community updates
• 00:51 USCAP recap, signed book giveaway, and free Digital Pathology 101 PDF
• 02:04 Partnerships, lab automation preview, and what’s coming in this episode
• 03:25 Multimodal deep learning for cervical cancer survival prediction
• 13:00 Why pathology may be a better response endpoint than radiology in neoadjuvant HNSCC immunotherapy
• 23:09 Ki-67 scoring in pulmonary neuroendocrine neoplasms: pathologists vs two AI systems
• 33:46 AI, digital morphology, and automation in hematologic diagnostics
• 43:29 qFibrosis, digital biomarkers, and the MAESTRO-NASH Phase 3 trial
• 51:57 Closing thoughts, community updates, and Easter promotion

Resources

1. Deep Learning Can Predict the Overall Survival of Cervical Cancer Based on Histopathological Image, Gene Mutation and Clinical Information
   https://pubmed.ncbi.nlm.nih.gov/41902378/
2. Modern Pathology-Driven Strategies in Neoadjuvant Immunotherapy for Head and Neck Squamous Cell Carcinoma: From Residual Tumor Quantification to Spatial and AI-Based Biomarkers
   https://pubmed.ncbi.nlm.nih.gov/41899621/
3. Ki-67 Proliferation Index in Pulmonary Neuroendocrine Neoplasms: Interobserver Agreement Among Pathologists and Comparison of Two Artificial Intelligence-Based Image Analysis Systems
   https://pubmed.ncbi.nlm.nih.gov/41898274/
4. Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations
   https://pubmed.ncbi.nlm.nih.gov/41897649/
5. Quantitative regression of qFibrosis with resmetirom: Exploratory histologic endpoints from the MAESTRO-NASH phase III clinical trial
   https://pubmed.ncbi.nlm.nih.gov/41895606/

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Paper Discussed in this Episode: High-Sensitivity Pan-Cancer AI Assessment of Lymph Node Metastasis via Uncertainty Quantification. Wang X, Chen Y, Liu X, et al. npj Digit. Med. (2026).

Episode Summary: In this episode, we explore a groundbreaking 2026 study that tackles the "black box" problem of medical AI. We dive into UPATHLN, a pan-cancer AI platform for detecting lymph node metastases that doesn't just try to be right—it explicitly knows when it might be wrong. By using an innovative "uncertainty" fail-safe, this system achieved an unprecedented 100% sensitivity while drastically cutting down pathologist workload.

In This Episode, We Cover:

• The Needle in the Haystack Problem: Why finding cancer in lymph nodes is crucial for patient survival and therapeutic decision-making, and why the sheer volume of rising cancer cases is overwhelming human pathologists.

• The Danger of "Overconfident Errors": How standard deep learning models stumble on rare, "long-tail" tumor variants. Standard AI is prone to making incorrect predictions with high certainty on data it hasn't seen before, leading to dangerous missed diagnoses.

• Meet UPATHLN - The Unified AI: Moving away from fragmented, organ-specific AI to a single, foundation-model-powered platform trained and validated on a massive dataset of 26,229 lymph nodes across 14 distinct primary organs.

• The "Fail-Safe" Mechanism (Uncertainty Estimation): How the researchers built a decoupled module that acts as a clinical safety net. Instead of forcing a guess, the AI flags "High Uncertainty" (HU) regions—like atypical cells or distracting elements like anthracotic pigment—and routes them directly for mandatory human review.

• The Results - 100% Rescue Rate: In independent testing, relying on the AI's diagnostic probability alone would have missed 60 metastases. However, the uncertainty module successfully intercepted all 60 of these initially missed cases, achieving a 100% conditional sensitivity, even on 7 rare cancer types the AI had never seen before during training.

• The Future of the Lab: How UPATHLN safely eliminated 73.2% of negative lymph nodes from manual review. By liberating pathologists from routine triage, the system frees up time for advanced, multi-dimensional precision oncology that goes beyond simple staging.

Key Takeaway: The key to safe clinical AI isn't just raw accuracy—it's failure awareness. By teaching AI to explicitly model its own uncertainty, the system intercepted all missed diagnoses, handled rare biological variants safely, and established a trustworthy, workload-efficient partnership between human experts and artificial intelligence

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Paper Discussed in this Episode:

Reliable classification of polyps based on artificial intelligence: a development and validation study. Julbø FMI, Henriksen AL, et al. eClinicalMedicine 2026;93: 103826.

Episode Summary:

In this journal club deep dive, we explore a groundbreaking 2026 study that tackles the massive bottleneck in gastrointestinal pathology caused by successful colorectal screening programs. We examine POLARIS, an AI triage system designed to safely clear over 50% of a pathologist's routine workload. But what happens when the algorithm fiercely disagrees with the human diagnosis? In a blinded showdown, the AI proves it's not just an efficiency tool—it might just be the ultimate safety net for catching high-risk cancer cells that human eyes overlook.

In This Episode, We Cover:

• The Pathology Bottleneck: Why the success of colorectal screening programs is drowning labs in biopsy slides, and how the subjective, visual nature of diagnosing polyps leads to dangerous inter-observer variability.

• The 5:2 Triage Strategy: How POLARIS categorizes gigapixel slide images into five biological classes (0 to 4) and translates them into two highly actionable buckets: "Review" (the complex and malignant) and "No Review Required" (normal tissue and routine tubular adenomas with low-grade dysplasia).

• Beating the "Clever Hans" Effect: How researchers prevented the AI from "cheating" by recognizing the digital fingerprints of different scanner brands, like Aperio vs. NanoZoomer. By using an image registration tool called elastix to perfectly align slides scanned on both machines, they heavily penalized the algorithm mathematically for relying on color profiles, forcing it to focus purely on biological morphology.

• The Showdown - Humans vs. AI: A blinded consensus review was conducted on 40 highly contentious cases where the AI aggressively disagreed with the original patient medical record. Three independent expert pathologists were brought in to break the tie without knowing the AI's or the original doctor's diagnosis.

• The Shocking Results: The expert panel sided with the AI over the original human diagnosis in a staggering 92.5% of the disputed cases, proving the established clinical "ground truth" isn't infallible.

• The RGBA Heat Map: How POLARIS functions as an active assistant, leaving normal tissue transparent (scaling the alpha channel to zero) while highlighting severe cellular atypia in glowing red, acting as a hyper-accurate topographical map for pathologists.

Key Takeaway:

AI in digital pathology isn't about autonomously replacing human experts; it's a hyper-sensitive navigational aid. By safely managing the flood of routine low-grade cases and accurately highlighting hidden high-risk dysplasias that exhausted human eyes miss, POLARIS corrects human errors and elevates the baseline standard of diagnostic care across the entire pipeline.

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Paper Discussed in this Episode: A Deep Learning Framework for Automated Triage of Breast Cancer Biopsies in Malaysia: A Simulation Study to Reduce Resource Consumption and Diagnostic Turnaround Time. Yudi Kurniawan Budi Susilo, Dewi Yuliana, Shamima Abdul Rahman, Siew Lian Leong. Clinical Breast Cancer 2026
.
Episode Summary: In this deep dive, we explore a revolutionary approach to a massive real-world healthcare bottleneck: agonizingly long diagnostic wait times in resource-constrained public hospitals
. We unpack a 2026 study that bypasses strict patient privacy red tape by using AI trained entirely on synthetic, computer-generated breast tissue images
. More importantly, the researchers built a "digital twin" of a Malaysian hospital to prove how an AI triage system could reorganize the diagnostic queue, catching aggressive cancers much faster while effectively conjuring new specialists out of thin air through massive time savings
.
In This Episode, We Cover:
• The "FIFO" Bottleneck: Why the traditional First-In, First-Out workflow traps critical malignant biopsies behind a mountain of benign cases (which make up 70-80% of biopsies), acting like a trauma surgeon forced to treat paper cuts before looking at a major emergency
.
• Solving the Data Paradox with GANs: How the team used Generative Adversarial Networks (StyleGAN2-ADA) to forge 10,000 synthetic whole slide images, achieving such high statistical realism (FID < 25) that human pathologists were fooled and gave a >90% plausibility rating
.
• The AI Triage Engine: A look into the Convolutional Neural Network built on a pre-trained ResNet50 architecture
. We discuss how it uses an attention-based Multiple Instance Learning (MIL) mechanism to break down billions of pixels into digestible patches, achieving a staggering 96.5% sensitivity—acting as a hyper-vigilant gatekeeper to ensure no cancers are missed
.
• Sim City for Pathology: How the researchers avoided testing on a live clinic and instead ran a Discrete-Event Simulation mimicking a chaotic public hospital for 250 days, factoring in chaotic arrival times and human reading delays
.
• The Shocking Results: The pure AI triage system plummeted turnaround time for suspicious cases by 38.3% (dropping from 7.24 days to 4.47 days), vastly outperforming hybrid or rule-based systems
.
• The Ripple Effect (Green Labs & Burnout): The system slashed pathologist workloads by 22.5% (saving 422 specialist hours annually) and reduced chemical reagent consumption by 15.2% by batch-processing the benign queue with standard chemicals
.
• The Reality Check: The critical limitations of synthetic data when faced with the messy realities of a physical hospital, including varying digital scanner color calibrations, IT infrastructure crashes, and local histological edge cases
.
Key Takeaway: AI in medicine isn't just about making the diagnosis—it's about fixing the workflow. By combining hyper-realistic synthetic data generation with discrete-event simulation, researchers proved that simply allowing an algorithm to sort a hospital's backlog can cut agonizing wait times for cancer patients by 38.3% and significantly relieve overburdened medical staff
. The digital twin of the hospital is already here, and it might just hold the cure for systemic healthcare gridlock

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Paper Discussed in this Episode:

Ki-67 Proliferation Index in Pulmonary Neuroendocrine Neoplasms: Interobserver Agreement Among Pathologists and Comparison of Two Artificial Intelligence-Based Image Analysis Systems. Teoman G, Turkmen Usta Z, Sagnak Yilmaz Z, Ersoz S. MDPI 2026.

Episode Summary:

In this journal club deep dive, we step into the lab to examine a direct comparison between expert human pathologists and artificial intelligence. We explore a 2026 study that evaluates how two different AI image analysis systems score the critical Ki-67 biomarker in Pulmonary Neuroendocrine Neoplasms (PNENs) alongside four experienced human experts. Unlike stories where AI and humans clash, this study explores a different exciting reality: Can AI perfectly match the human gold standard to automate and standardize a highly tedious, labor-intensive medical process?

In This Episode, We Cover:

• The Diagnostic Challenge of Lung NENs: Understanding Pulmonary Neuroendocrine Neoplasms, a biologically diverse group of lung tumors ranging from slow-growing typical carcinoids to highly aggressive large cell neuroendocrine carcinomas. We discuss why precise classification is critical for predicting patient outcomes and guiding treatment.

• The Spotlight Biomarker (The Speedometer): ◦ Ki-67: The definitive marker of active cellular proliferation, essentially acting as the tumor's "speedometer". While not formally incorporated into the WHO grading criteria for lung NENs, it is a vital clinical tool used to distinguish low-grade from high-grade tumors and identify biologically aggressive lesions.

• The Showdown - Humans vs. AI: Four experienced pathologists go head-to-head with two digital heavyweights—the Roche uPath Ki-67 and the Virasoft Virasight Ki-67 algorithms. They analyzed 63 cases across different tumor subtypes, meticulously evaluating approximately 2,000 cells per predefined tumor hotspot.

• Round 1 - Impressive Human Concordance: The human experts achieved near-perfect interobserver agreement (an Intraclass Correlation Coefficient of 0.998) when utilizing pre-selected hotspot regions, proving that standardized manual counting by experts is highly reliable.

• Round 2 - AI Meets the Gold Standard: Both AI systems demonstrated massive, statistically significant correlations with the human experts' assessments. The AI reliably stratified the lung tumors into low, intermediate, and high-risk clinical categories without systematic bias, proving the algorithms can match human accuracy.

• The Future of the Lab: Why AI shouldn't replace pathologists, but rather serve as a reproducible, objective assistant in the pathology lab. We discuss how automated AI analysis can reduce observer fatigue, enable rapid assessment of large tumor areas, and standardize testing across institutions, despite current roadblocks like algorithm complexity and a lack of wide accessibility.

Key Takeaway:

Artificial intelligence doesn't have to disagree with humans to prove its profound clinical worth. By successfully matching the excellent accuracy of top pathologists, these AI systems proved they can reliably handle the exhausting, subjective task of tumor cell counting. This paves the way for faster, highly standardized tumor evaluation, which could ultimately lead to more consistent and reliable prognostic diagnoses for lung cancer patients

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Deep Learning Can Predict the Overall Survival of Cervical Cancer Based on Histopathological Image, Gene Mutation and Clinical Information. Shen J, Miao Z, Wang L, et al. IET Systems Biology 2026.

Episode Summary: In this deep dive, we explore a groundbreaking 2026 study that uses multimodal deep learning to act as a "master diagnostician" for cervical cancer. We examine what happens when an AI is fed a combination of standard clinical data, cutting-edge genetic sequencing, and century-old H&E tissue slides. The results force us to rethink how cancer operates: what happens when the genetic "blueprint" of a tumor lies to us, and the real biological truth is hiding in the seemingly chaotic pink and purple pixels of the connective tissue?

In This Episode, We Cover:

The Murky Diagnostics of Oncology: Understanding why predicting an individual patient's overall survival (OS) in cervical cancer is profoundly difficult. Getting this prediction wrong means risking either lethal undertreatment (distant metastasis) or subjecting stable patients to devastating overtreatment toxicities.

The Three Modalities (The Suspect, The DNA, and The Security Footage):
Clinical Data: The "suspect's description," utilizing standard patient metrics like age and tumor stage.
Molecular Data: The genetic "blueprint" and somatic gene mutations. The AI isolated major red flags like RGR, DBN1, and CALCR mutations, which drive metastasis and signal poor prognosis.
Histopathological Images (H&E): The "security footage" showing the physical tissue battlefield via whole slide images.

The Model Showdown: Researchers trained a deep learning model (ResNet18) and fused these modalities using Multimodal Compact Bilinear (MCB) fusion. The AI was tasked with classifying patients into short-term (under 3 years) or long-term (over 3 years) survival, and it was rigorously validated on a completely independent dataset (PUMCH) to ensure generalizability.

Round 1 - The Genetic Curveball: Despite being the cell's source code, genetic mutation data was the absolute worst predictor of survival, achieving an AUC of just 0.559. Adding it to the AI actually caused the "curse of dimensionality," making the model worse by overwhelming it with mathematical noise.

Round 2 - The AI's "Aha!" Moment: The tissue phenotype dictates what actually happens. Fusing simple clinical data (age) with H&E images achieved a highly accurate 0.783 AUC. Even more shockingly, for aggressive short-term survival cases, the AI didn't focus heavily on the tumor itself. It looked at the stroma (connective tissue), deducing on its own that the host's inflammatory battleground dictates the lethality of the disease.

The Future of the Lab: How automated quality control (HistoQC) and mathematical techniques (Macenko color normalization) strip away lab technician error and chemical dye variations. We also look ahead to how hyperspectral imaging might soon reveal the foundational chemical signatures of living cells.

Key Takeaway: Throwing more data at an algorithm isn't always better. By successfully extracting profound biological truths from routine, inexpensive H&E slides, the AI proved that we don't necessarily need $1,000 genomic sequencing panels to accurately predict prognosis. The physical manifestation of the tumor microenvironment tells us exactly who is winning the battle, paving the way for accessible precision medicine

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Paper Discussed in this Episode:

Modern Pathology-Driven Strategies in Neoadjuvant Immunotherapy for Head and Neck Squamous Cell Carcinoma: From Residual Tumor Quantification to Spatial and AI-Based Biomarkers. Annabella Di Mauro, Rossella De Cecio, Saverio Simonelli, et al. Cancers (MDPI) 2026.

Episode Summary: In this journal club deep dive, we explore a paradigm-shifting 2026 paper that fundamentally fractures our reliance on traditional radiology in head and neck cancer. We uncover a shocking clinical disconnect where seemingly devastating CT scans mask miraculous microscopic victories. When neoadjuvant immunotherapy unleashes the immune system, why does the tumor often look like it's growing on imaging? And how is pathology stepping out of the shadows to become the ultimate arbiter of biological truth, dictating precise surgical and medical oncology decisions?

In This Episode, We Cover:

The Trojan Horse of Imaging (Pseudoprogression): Why traditional CT scans are failing us in the immunotherapy era. Immunotherapy causes an influx of T-cells and inflammation that physically expands the tissue, tricking radiologists into diagnosing progressive disease when the cancer is actually being systematically dismantled from the inside out.

The New Gold Standard - RVT: Why measuring the "shadow" of the tumor is obsolete. We discuss why pathologists are pivoting away from size and instead strictly quantifying Residual Viable Tumor (RVT) to determine the exact percentage of living, metabolically active carcinoma cells left behind.

The "Starry Sky" Phenomenon: Tumors don't shrink like an ice cube melting from the outside in. We discuss how immune cells tunnel into the tumor, shattering it into a discontinuous "starry sky" pattern—scattered, radiologically occult microscopic islands of surviving cancer hidden across a vast sea of therapy-altered stroma.

Compartmental Dissociation (The Nodal Force Field): A terrifying clinical reality where a patient can achieve a 100% complete pathological response at the primary mucosal site, but simultaneously harbor highly viable, proliferating cancer in their cervical lymph nodes. We explore how tumors hijack M2 macrophages to build a localized, cytokine-driven "force field" that neutralizes systemic T-cells the second they enter the node.

The Future - High-Definition Spatial Biology: How AI-assisted digital pathology and spatial transcriptomics act as the "GPS tracking" or "sports analytics" of the tumor microenvironment. By mapping the exact coordinates of immune and cancer cells, tumor boards can confidently de-escalate toxic post-operative treatments for clear patients, or accurately target specific immunosuppressive resistance niches.

Key Takeaway: Traditional imaging measures the volume of the battlefield, not the volume of the remaining enemy. By redefining therapeutic response through the microscopic lens of Residual Viable Tumor and AI-driven spatial biology, pathologists are no longer just staging dead tissue. They are now the central navigators of precision oncology, guiding the real-time escalation and de-escalation of patient care based on the true biological reality of the tumor

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Paper Discussed in this Episode: Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations. Alnoor F, Mukherjee S, Menon MP, Ng D, Li P, Ohgami RS. Diagnostics 2026.

Episode Summary: In this deep dive, we explore how hematology labs are tackling a massive rise in diagnostic complexity combined with persistent staffing shortages. The solution isn't just working harder—it's an entirely new workflow powered by robotics and AI. We unpack a comprehensive 2026 review that looks at the cutting-edge transformation of hematopathology, moving from manual microscopes to collaborative robots (cobots), digital morphology, and AI-driven genomic analysis. Can machines handle the grueling pre-analytical work and help experts diagnose leukemia faster and more accurately?

In This Episode, We Cover:

• The Modern Lab Crisis: How the latest WHO and International Consensus Classification (ICC) frameworks demand high-volume, multi-modal genomic and morphologic data, stretching human pathologists to their limits.

• Enter the "Cobots": Collaborative robots are taking over the repetitive benchwork. We discuss systems like the UR5 cobots in Denmark that sort 3,000 blood tubes a day, and the Pramana Spectral HT robotic-arm scanners that digitize over 1,000 slides daily, freeing up human staff for higher-level tasks.

• The Digital Eye (Morphology & AI): How platforms like CellaVision and Scopio turn glass slides into AI-analyzed data. ◦ Peripheral Blood: AI pre-classifies cells with 85-98% concordance to manual microscopy, prioritizing blasts and abnormal cells for expert review to improve efficiency. ◦ Bone Marrow: Deep learning isn't just counting cells; it's accurately quantifying reticulin fibrosis and identifying leukemia subtypes with human-level performance.

• Flow Cytometry Gets an Upgrade: High-dimensional flow cytometry data meets deep learning. AI models are now achieving expert-level performance in classifying mature B-cell neoplasms and accurately distinguishing acute leukemias from non-leukemic samples.

• The Molecular Frontier: AI is making sense of complex genomic datasets. We discuss breakthroughs like the MARLIN neural network, which achieves rapid epigenomic classification of acute leukemia in under two hours, and how AI assists in tracking measurable residual disease (MRD) longitudinally.

• The Economics of Automation: Digital pathology is a smart financial investment. We review projections showing potential savings of $18 million over five years for integrated health systems, driven by improved efficiency, higher throughput, and fewer diagnostic errors.

Key Takeaway: The integration of artificial intelligence and robotics is not meant to replace hematopathologists; rather, these technologies serve as essential scaling tools designed to absorb grueling physical labor and routine analytical tasks. By building a workflow where machines handle the sorting, scanning, and initial pattern recognition, experts can focus their time on final diagnostic synthesis—ultimately delivering faster, more precise patient care

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Paper Discussed in this Episode:

Quantitative regression of qFibrosis with resmetirom: Exploratory histologic endpoints from the MAESTRO-NASH phase III clinical trial. Schattenberg JM, Bedossa P, Guy CD, et al. Journal of Hepatology 2026; https://doi.org/10.1016/j.jhep.2026.03.021.

Episode Summary: In this deep dive, we explore how artificial intelligence is revolutionizing the way we measure liver disease recovery. We examine a groundbreaking 2026 Phase III clinical trial (MAESTRO-NASH) that compared traditional human pathologist staging against an AI-driven digital pathology tool called qFibrosis. The study forces us to reconsider our clinical gold standards by asking: what if AI can detect subtle biological healing that the experienced human eye completely misses?

In This Episode, We Cover:

• The Silent Epidemic: Understanding Metabolic dysfunction-associated steatohepatitis (MASH), a progressive, active form of fatty liver disease linked to cardiovascular risk and cirrhosis. We discuss why precisely tracking the reversal of liver fibrosis is crucial for patient outcomes.

• The "Ordinal" Problem: Why the current "gold standard"—human pathologists assigning a simple ordinal score (like Stage F1, F2, or F3)—is subjective and fails to capture the dynamic, nuanced reality of fibrosis progression and regression.

• The AI Microscope (SHG & qFibrosis): ◦ SHG (Second Harmonic Generation): An imaging technique that takes advantage of the physical properties of collagen to map out the three-dimensional architecture of the liver. ◦ qFibrosis: An AI-driven analysis tool that evaluates up to 184 distinct features of liver collagen (like string length, width, and intersections) across different regions of the liver lobule, providing a continuous, hyper-detailed assessment rather than a basic category.

• The Showdown - Humans vs. AI: Using data from 966 patients in the MAESTRO-NASH trial, we compare how human pathologists and the AI evaluated liver biopsies at baseline and week 52 to test the efficacy of the drug resmetirom.

• The AI's "Aha!" Moment (Seeing the Invisible): The most shocking finding of the study occurred in the "non-responder" group. Even when human consensus reads declared certain patients had no histological improvement, the AI detected significant, continuous reductions in liver fibrosis (qFC scores). The digital pathology tool was able to pick up on subthreshold, early matrix remodeling that was entirely invisible to standard manual scoring.

• Mapping the Liver's Healing: The AI proved its biological accuracy by successfully linking its spatial data to real-world clinical outcomes. The AI found that specific regional changes—particularly in the portal tract—strongly correlated with non-invasive liver stiffness tests like Magnetic Resonance Elastography (MRE).

Key Takeaway: AI isn't here to replace human pathologists; it is a hyper-sensitive tool designed to uncover hidden data patterns. By detecting continuous, region-specific changes in liver collagen, AI digital pathology can identify early therapeutic responses to MASH treatments that traditional staging misses, fundamentally changing how we track disease reversal and personalize medicine

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Paper Discussed in this Episode: Digital Twins in Neuro-Oncology: A Systematic Review of Current Implementations, Technical Strategies, and Clinical Applications. Annie Singh, Fatima Ahmad Qureshy, Angelica Kurtz, Moinak Bhattacharya, Prateek Prasanna, and Gagandeep Singh. Radiology: Imaging Cancer 2026; 8(2).

Episode Summary: In this journal club deep dive, we explore a groundbreaking 2026 systematic review of digital twins in neuro-oncology. We step past the buzzwords and examine how exact virtual copies of patient brains are being built to safely simulate dangerous radiation regimens and drug combinations for highly aggressive tumors. This forces us to ask an uncomfortable question: Are we just slapping the label "digital twin" on static algorithms, or are we actually building living, continuously updating virtual copies of patient tumors? Furthermore, what happens to clinical ethics when a perfect simulation predicts a patient's tumor will resist every standard line of therapy before they even try it?

In This Episode, We Cover:

• Defining the True Twin: We break down what separates a standard, static computational model from a true digital twin. A real digital twin requires closed-loop optimization with continuous, real-time feedback from a patient's actual biological response—a critical feature shockingly missing in 13 out of the 21 reviewed models.

• The Dominance of Old-School Math: Why the most advanced simulations aren't relying solely on modern machine learning, but rather mechanistic models built on reaction-diffusion differential equations. We explain how these models calculate variables like tumor cell density, proliferation rate, and tissue carrying capacity to simulate literal physical pressure in the brain. Transparency and trust trump "black box" AI when neuro-oncologists are making life-altering surgical decisions.

• The AI Visual Forecaster: How cutting-edge AI diffusion models, like BrainMRDiff and ImmunoDiff, serve as hybrid partners to these math equations. These tools take complex biological calculations and generate high-fidelity, anatomically consistent visual MRIs to accurately forecast how a tumor will morph post-treatment.

• Grading Their Own Homework: A look at the PROBAST risk of bias assessment, which revealed that while outcome accuracy seems high, many models suffer from overfitting, data leakage, and a massive lack of external validation.

• The Big Bottlenecks - Broken Pipes and Locked Safes: We discuss the roadblocks keeping this out of the bedside. Specifically, the glaring lack of open-source code (only 6 of 21 studies shared theirs) makes standardization impossible. We also examine the engineering nightmare of multimodal data fusion—combining asynchronous streams of MRIs, genomics, and tissue pathology into a real-time model.

Key Takeaway: While digital twins represent a monumental leap toward true precision medicine, the field is currently bottlenecked by proprietary secrecy and broken data infrastructure. Until the scientific community embraces open-source code sharing and hospital systems solve the complex engineering challenge of real-time multimodal data integration, these revolutionary tools will remain isolated research projects rather than the living clinical tools they are meant to be

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Why do digital pathology projects get harder once the real workflow starts?

In this USCAP 2026 conversation, I talk with Robert Moody from Hamamatsu and Jake Eden from Agilent about what the conference theme, MAKING CONNECTIONS, looks like in actual digital pathology implementation. This was not just a conversation about products. It was a conversation about workflow. We talked about why consistent staining matters before scanning, why strong partnerships need a shared vision, and why labs increasingly want a simpler point of contact as they move into digital pathology.

One point I really liked is that the value of a partnership is no longer just in combining components. It is in reducing complexity for the lab. Robert and Jake explain how vendors increasingly act as guides during digital transformation, helping customers navigate technical decisions, implementation steps, and the many stakeholders involved beyond pathology itself. That includes IT, information security, legal, finance, and lab operations.

Another key theme is that no two deployments look the same. Some labs are centralized. Some are hub-and-spoke. Some outsource parts of the workflow. That is why future-proofing came up so strongly in this episode. Jake talks about keeping options open with open, agnostic workflows, and Robert makes the practical point that the most expensive thing you can do is the same implementation twice.

Key highlights

• [00:22] Why this episode moves from high-level partnerships to what they look like in the lab
• [02:33] Why staining consistency matters for successful digital workflows
• [03:14] Shared vision, relationships, and why partnerships start with people
• [05:29] The idea of a single point of contact to reduce complexity for labs
• [08:32] Why vendors have become digital pathology guides
• [10:03] Why every deployment is unique
• [14:22] Future-proofing and choosing open, agnostic workflows
• [15:46] Why doing the same implementation twice is the expensive mistake to avoid

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Why does digital pathology adoption move faster in some places than others?

In this USCAP 2026 conversation, I sat down with Robert Moody and Fumiya Fuji from Hamamatsu to talk about what the conference theme, MAKING CONNECTIONS, really looks like in practice. This was not just a scanner conversation. It was a workflow conversation.

We talked about why digital pathology has shifted from a scanner-first mindset to a solution-first one, and why that matters for labs trying to build workflows that actually work. Robert explained why partnerships now need to happen earlier, with software, hardware, and execution teams involved from the start. Fumiya added a global perspective, comparing adoption drivers across the US, Japan, Europe, and Canada, and explaining why local support systems, ROI, geography, and government backing can all change the pace of adoption.

One point I especially liked was this: digital pathology is not one product. It is an ecosystem. And if one component fails, the whole workflow can break down. That is why connected thinking matters so much right now. This episode is really about how companies, labs, and partners are learning to work more like a team.

Key highlights

• [00:00] Why MAKING CONNECTIONS fits digital pathology so well
• [01:37] Why partnerships matter beyond the scanner
• [04:29] The shift from scanner-first to solution-first
• [04:58] How adoption differs across the US, Japan, Europe, and Canada
• [09:01] Why global collaboration inside Hamamatsu matters
• [10:50] How partnerships move from paper to real-world execution
• [12:55] Why does the USCAP show floor show a more connected industry
• [14:37] Why the next phase of digital pathology depends on interoperability and connected workflows

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What makes digital pathology feel so hard to enter, even for smart people already working around it?

In this special USCAP conversation, Stephanie Fullerton from Hamamatsu turns the tables and interviews me about Digital Pathology 101 — the book I wrote for people who are starting or continuing their digital pathology journey.

We talk about why the book is not meant to be an exhaustive manual, but a practical framework. A way to help people see the full picture, ask better questions, and understand how the pieces of digital pathology fit together.

One of the biggest themes in this conversation is that digital pathology is a team effort. It is not just pathology. It involves scanners, software, image analysis, engineers, vendors, and people who often do not speak the same professional language.

That matters because sometimes getting the right answer starts with asking the right question.

We also talk about the challenge of translating expert knowledge into beginner-friendly language, why vendors often become guides as labs go through digital transformation, and why I think a shared vocabulary can make implementations smoother and more collaborative. Toward the end, we shift into the fun side of USCAP: signed book giveaways, stickers, pins, and ways to make connections at the conference.

Topics discussed

• [00:03] Why Stephanie interviewed me this time, and the idea behind Digital Pathology 101
• [01:07] What the book is actually for: a framework, not a one-size-fits-all manual
• [04:07] The hardest part of writing for beginners without talking down to them
• [06:26] Why digital pathology implementation feels like a mountain, and how to lower the barrier
• [08:15] Why a shared vocabulary matters in digital pathology teams
• [09:44] Translating between pathologists, engineers, vendors, and marketing
• [11:26] Why vendors and partners often become guides during digital transformation
• [12:33] Who the book is for, including students and early-career professionals
• [13:33] Book signing, giveaways, and where to find me at USCAP
• [19:05] Stickers, pins, and why small things can help start real conversations at conferences

Resources mentioned

• Digital Pathology 101
• Hamamatsu Booth 312 at #USCAP2026 in San Antonio, Texas
• My histology and microscopy videos on YouTube

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Paper Discussed in this Episode:

The Performance of Artificial Intelligence in Classifying Molecular Markers in Adult-Type Gliomas Using Histopathological Images: Systematic Review. Almaabreh O, Al-Dafi R, Tabassum A, Othman A, Abd-alrazaq A. J Med Internet Res 2026; 28: e78377.

Episode Summary: In this deep dive of the Digital Pathology Podcast, we explore the intersection of human limitations and computational power. Following the 2021 World Health Organization mandate requiring molecular data to diagnose adult-type gliomas, pathology has faced a massive bottleneck. Can artificial intelligence look at a standard pink-and-purple tissue slide and accurately predict hidden genetic mutations to serve as a diagnostic shortcut? We unpack a massive 2026 systematic review that evaluates the architectures, the "data diets," and the structural hurdles of using AI to "see the invisible".

In This Episode, We Cover:

• The 2021 WHO Diagnostic Shakeup: How the World Health Organization shifted glioma diagnosis from pure visual morphology (judging a book by its cover) to requiring precise genetic spelling (finding a typo on page 42), making the diagnostic process incredibly slow and expensive.

• The Targets - IDH vs. 1p/19q: Why AI models are highly proficient at spotting the metaphorical "canyon" carved by early metabolic IDH mutations, but struggle to find the subtle visual clues of 1p/19q chromosomal codeletions.

• The AI Toolkit - CNNs, MIL, and Transformers: ◦ CNNs (like DenseNet121): The heavy lifters of medical imaging, analyzing local cell structures and edges by constantly reusing foundational visual features. ◦ Multiple Instance Learning (MIL): The brilliant algorithmic solution to the excruciating human labor of pixel-by-pixel tumor annotation, allowing the AI to mathematically figure out what cancer looks like using only slide-level labels. ◦ Hybrid Models: By combining the microscopic focus of CNNs with the zoomed-out, global contextual awareness of Transformers, these models achieved the highest average accuracy at 92.80%.

• The "Data Diet" and Domain Shift: The critical danger of training AI exclusively on single, homogeneous databases like the TCGA. We discuss why an algorithm that performs perfectly in a pristine "test kitchen" completely panics and drops in performance when faced with the varied stains, slice thicknesses, and scans of real-world community hospitals.

• Multimodal Medicine: The revelation that AI models perform vastly better when fed diverse data streams, such as combining slide images with MRI scans and clinical notes. Implementing this necessitates a monumental structural integration between historically siloed hospital departments like radiology and pathology.

Key Takeaway: AI is not replacing pathologists tomorrow; it is stepping into the co-pilot seat. While hybrid models show immense promise, their true standalone clinical adoption depends on breaking free from narrow training data, overcoming domain shift, and fundamentally restructuring our hospitals to feed these algorithms the multimodal context they need to thrive

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Paper Discussed in this Episode:

A comprehensive European Colorectal Cancer Cohort dataset. Holub P, Törnwall O, Garcia Alvarez E, et al. Sci Data (2026). https://doi.org/10.1038/s41597-026-06822-2.

Episode Summary: In this journal club edition of the Digital Pathology Podcast, we explore a monumental effort to clear up the diagnostic "muddy waters" of Colorectal Cancer (CRC). We examine a groundbreaking 2026 paper detailing a massive European dataset of 10,780 CRC patients that provides an unprecedented "playground" for artificial intelligence. This episode asks how we can accurately predict cancer recurrence years down the line, and explores whether a 70-terabyte multimodal dataset might help algorithms uncover hidden biomarkers that could make traditional tumor staging completely obsolete.

In This Episode, We Cover:

• The "Gray Area" of Oncology: Understanding Stage II Colorectal Cancer, where primary tumors are removed but clear lymph nodes leave oncologists gambling on whether highly toxic chemotherapy is necessary to prevent microscopic recurrence.

• A Continental AI Playground: A look at the sheer scale of the BBMRI-ERIC consortium's dataset: 10,780 patients from 26 biobanks across 12 countries, purposefully prioritized to include at least five years of clinical follow-up data.

• The Three-Dimensional Disease Map: How the dataset links standard clinical records (the "street addresses") with Whole Genome Sequencing blueprints and 26 terabytes of gigapixel Whole Slide Images (the "satellite view") to give machine learning models a complete biological picture.

• The Messy Reality of Raw Hospital Data: Why structural translation to OMOP and openEHR isn't enough. We highlight the terrifying logical errors caught by the consortium's automated plausibility scripts—from negative treatment durations to patients receiving chemotherapy after being marked as deceased.

• Hacking GDPR for Rapid Research: How the project uses envelope encryption (Crypt4GH) and a 14-day "time-limited veto" system to securely grant researchers global, free access, proving that patient privacy and rapid scientific speed can seamlessly coexist.

Key Takeaway: If deep learning algorithms trained on thousands of pristine digital slides and genomic blueprints can identify new morphological biomarkers and predict cancer recurrence with pixel-level accuracy, we may be looking at the beginning of the end for the century-old TNM staging system. This democratized dataset finally provides the massive statistical power needed to fundamentally redefine patient stratification

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Paper Discussed in this Episode:

Artificial Intelligence and Its Applications in Oral and Maxillofacial Pathology. Veremis B. Dent Clin North Am. 2026 Apr;70(2):403-416.

Episode Summary: In this Journal Club edition of the Digital Pathology Podcast, we explore a wild paradox at the bleeding edge of diagnostic medicine. We examine a 2026 paper on artificial intelligence in oral and maxillofacial pathology that reveals a fascinating reality: while highly advanced AI models can match human experts in detecting diseases, their clinical rollout is completely blocked by a surprisingly analog problem. We unpack why a 15-second difference in a laboratory dye bath might thwart billion-dollar neural networks and what this means for the future of the pathology lab.

In This Episode, We Cover:

• The Baseline - Matching Human Experts: How AI currently performs at human-expert levels for straightforward diagnostic tasks, such as detecting squamous cell carcinoma.

• The Predictive Frontier (Prognostication): How AI goes beyond binary diagnosis to evaluate complex spatial relationships—like calculating the precise micrometer distance between every single tumor-infiltrating lymphocyte and the invading edge of a carcinoma. We discuss the holy grail of predicting malignant transformation in oral premalignant disorders.

• The Analog Roadblock - Pre-analytical Variance: Why the physical, multi-step process of turning a tissue biopsy into a glass slide using H&E (hematoxylin and eosin) staining introduces massive data variability that severely confuses AI models.

• The "Mojave Desert" AI Trap: How human brains abstractly interpret a dark pink cell, while an AI algorithm sees a fundamentally different mathematical environment of numerical RGB pixel values. We discuss why an algorithm trained perfectly on one lab's specific slides will completely fail when fed slides from a different lab with slight chemical variations, much like a self-driving car trained in the desert crashing in a blizzard.

• The Data Drought: Why we desperately need millions of whole slide images from thousands of different laboratories to train robust, open-source AI models, and why these multi-institutional, standardized public datasets simply don't exist yet.

• The Ultimate Dilemma for Local Labs: Will the inevitable adoption of AI diagnostic tools force independent pathology labs to abandon their unique, decades-old tissue preparation methods in favor of a single, universally mandated global standard for tissue fixation and staining?.

Key Takeaway: The true bottleneck for AI in oral pathology isn't a lack of computational horsepower; it is analog inconsistency. Until the pathology field can standardize pre-analytical tissue preparation and build massive, publicly available datasets, highly sophisticated AI algorithms will remain isolated in the research lab instead of fulfilling their massive potential in everyday clinical diagnostics

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What happens when AI looks strong in a paper, but the workflow still isn’t ready?

In DigiPath Digest #40, I reviewed five recent papers across kidney pathology, oral and maxillofacial pathology, glioma biomarker prediction, digital twins in neuro-oncology, and a major European colorectal cancer cohort. A common theme kept coming back: good performance is not the same thing as real-world readiness.

We started with kidney biopsies and the challenge of assessing interstitial fibrosis and tubular atrophy, where AI shows promise but still does not fully agree with humans. That led into a bigger point I keep seeing in digital pathology: our “ground truth” is often based on human interpretation, and human interpretation has variability too.

From there, I looked at AI in oral and maxillofacial pathology, where the field is still early and one major bottleneck is the lack of strong public datasets. Then I discussed a systematic review on adult-type gliomas showing that multimodal models performed better than unimodal ones, which makes sense when you think about how pathologists actually work: we do not diagnose from one input alone.

I also covered a systematic review on digital twins in neuro-oncology. The idea is exciting, but the paper makes it clear that reproducibility, public code, multimodal integration, and external validation are still limiting factors.

And finally, I talked about a paper I really liked: a large European colorectal cancer cohort built across 26 biobanks in 12 countries. That kind of harmonized, quality-checked dataset matters. A lot. Because better AI starts with better data.

In this episode, I discuss:

• Why AI vs human comparisons are harder than they first look
• the “gold standard paradox” in pathology
• Why multimodal AI keeps outperforming unimodal models
• What is holding digital twins back from broader use
• Why curated multicenter datasets are so important for digital pathology research

Resources mentioned:

• Digital Pathology 101 pdf copy
• Pathology AI Makeover Course
• DigiPath Digest AI-powered paper summaries

Papers discussed:

• https://pubmed.ncbi.nlm.nih.gov/41830415/
• https://pubmed.ncbi.nlm.nih.gov/41826004/
• https://pubmed.ncbi.nlm.nih.gov/41824546/
• https://pubmed.ncbi.nlm.nih.gov/41823607/
• https://pubmed.ncbi.nlm.nih.gov/41820399/

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Paper Discussed in this Episode:

Assessing interstitial fibrosis and tubular atrophy in kidney biopsies artificial intelligence versus humans. Farris AB, Zukić D, Solez K. Current Opinion in Nephrology and Hypertension. March 16, 2026.

Episode Summary: In this journal club deep dive on the Digital Pathology Podcast, we explore the intense debate over quantifying chronic kidney disease progression. We unpack a fresh 2026 study comparing artificial intelligence to human pathologists in assessing interstitial fibrosis and tubular atrophy. If top experts can't agree on a diagnosis due to human subjectivity, can an AI trained on their imperfect data provide a better standard? We explore what happens when pixel-perfect machines clash with nuanced human medical judgment.

In This Episode, We Cover:

• The Clinical Stakes of Kidney Scarring: Why interstitial fibrosis (the scarring of tissue spaces between filtering units) and tubular atrophy (shrinking and collapsing functional tubes) are the primary surrogate measures for tracking chronic kidney disease. We discuss how a mere 10% diagnostic variance can drastically alter a patient's medication regimen, dialysis prep, or transplant eligibility.

• The Flaw in the "Gold Standard": We break down the "interobserver variability" problem—why two highly trained, board-certified pathologists can look at the exact same biopsy slide and give two completely different mathematical assessments of the damage.

• How the AI Actually Works (Mapping the Neighborhood): A look at "indirect assessment through kidney compartment segmentation," where the AI acts as a digital surveyor. It identifies cellular fences like glomeruli and tubules, establishing microscopic "zoning laws" before it begins counting the damaged tissue.

• The Proofreader vs. The Literary Critic: Why studies show a persistent "lack of complete concordance" between human and machine. We discuss how AI hyper-focuses on mathematical pixel intensity and mistakes physical slide artifacts (like a folded piece of tissue) for severe disease. Meanwhile, human pathologists act as "literary critics," easily filtering out the visual noise using clinical context.

• The Humans + AI Synergy: The ultimate endgame isn't replacing pathologists, but combining the tireless mathematical consistency of AI with the complex contextual reasoning of humans to create a highly advanced co-pilot system.

Key Takeaway: The lack of perfect agreement between AI and human pathologists isn't a failure, but rather evidence that they perform fundamentally different types of analysis. AI excels at tedious, reproducible quantification that eliminates human visual fatigue, but it lacks contextual judgment. By adopting a "humans + AI" workflow, the medical field can stabilize crucial kidney measurements and elevate the pathologist to a true diagnostic synthesizer, ultimately leading to more effective patient care

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Paper Discussed in this Episode:

Clarifying validation terminologies in healthcare. Amanda Dy, Sandra M. Buetow, Andrew J. Bredemeyer, et al. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02471-2.

Episode Summary:

In this deep dive, we unpack the silent chaos surrounding a single, universally used word in healthcare innovation: "validation". Exploring a 2026 paper by the Pathology Innovation Collaborative Community (PIcc), we uncover how differing definitions of this word across AI developers, hospital directors, regulators, and venture capitalists can lead to massive miscommunications, millions of wasted dollars, and compromised patient safety. We ask the critical question: when a developer says an AI tool is "validated," what are they actually selling you?

In This Episode, We Cover:

• The "Chameleon Word" of Healthcare: Tracing the evolution of "validation" from its Latin roots, to its 1940s use in physical measurement accuracy, and its 1962 shift into hold-out testing. Today, the word functions simultaneously as an evidence claim, a lifecycle activity, and a quality label, creating a fractured meaning across disciplines.

• The AI/ML Trap (Three Shades of Validation): Why an AI developer claiming a model is "validated" might just mean they checked the raw data for corrupt files (dataset validation) or tuned the model's math in the lab (validation data). Calling a model "validated" after internal cross-validation severely misrepresents its readiness for actual clinical deployment.

• The Clinical Lab Reality Check (Analytical vs. Clinical): The crucial difference between analytical validation (proving a tool is technically perfect, like a thermometer) and clinical validation (proving the tool actually helps diagnose patients correctly). We also explore why the gold-standard US lab framework, CLIA, completely abandons the word "validation" in favor of establishing "performance characteristics" that require rigorous, site-specific verification.

• The Regulatory and Business Minefields: How geography alters the legal definition, with the FDA focusing on intended use while European frameworks (IVDR) encompass entire lifecycle risk management. Furthermore, we discuss why "business validation" (securing investor funding) does not equate to clinical safety or regulatory readiness.

• The "Lightweight" Solution: The authors don't propose a massive new dictionary; instead, they advocate for simple, lightweight qualifiers. Teams must stop using "validation" as a binary yes/no label and instead explicitly define the context—stating exactly what phase, reference standard, and operational conditions were tested.

Key Takeaway:

The word "validation" has morphed into a pseudoscientific label of trust that can mask a product's true readiness. To prevent dangerous misalignments in medical innovation, interdisciplinary teams must demand explicit context: never just accept that a tool is "validated" without asking "validated for what exactly?"

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Paper Discussed in this Episode:

Deep learning-based histopathological classification and subclassification of benign and malignant salivary gland tumors. Weber A, Schuster D, Heyer J, Becker C, Burkhardt V, Werner M, Spörlein A, Bronsert P, Schulz T. European Archives of Oto-Rhino-Laryngology 2026.

Episode Summary: In this journal club deep dive of the Digital Pathology Podcast, we explore a chaotic microscopic landscape to see if artificial intelligence can master one of the most high-pressure diagnostic environments in medicine. We examine a groundbreaking 2026 study on rare salivary gland tumors, exploring how state-of-the-art AI models performed when tasked with distinguishing benign lesions from complex malignancies. We uncover where the AI achieved absolute perfection, where it catastrophically failed, and why its "mistakes" might just be a window into hidden biological truths.

In This Episode, We Cover:

• The High-Stakes Minefield of Salivary Glands: Why diagnosing these tumors is a delicate and complex task. With 36 potential entities and a practically zero margin for error, misdiagnoses can lead to devastating revision surgeries and permanent facial nerve palsy for the patient.

• Training the Machine: How researchers used 20 years of slide data and the "Reinhard color normalization method" to mathematically standardize color palettes. This prevented the AI from "cheating" by simply memorizing fading colors or specific lab stains.

• The AI Arsenal - CNNs vs. Vision Transformers: A look at the diverse algorithms deployed in the study, ranging from convolutional neural networks (like Xception and ConvNeXt) that scan local pixels, to Vision Transformers that analyze global image context, processing massive slides tile by tile.

• The Perfection of Binary Triage: The stunning success of the AI in the initial benign vs. malignant test. Models like Xception achieved a 100% Negative Predictive Value (NPV), meaning they never missed a single cancer, proving their potential as a flawless morning triage tool for pathology labs.

• The Subclassification Wall: Why the AI bombed when trying to identify the specific type of malignant tumor (like squamous cell or acinic cell carcinoma). We explore the deep learning rules of data volume and tissue heterogeneity, and why rare, morphologically chaotic diseases effectively starve algorithms of the data they need.

• Explainable AI & The "Clever Hans" Dilemma: By using Class Activation Maps (heat maps), researchers tracked the AI's "eyes". While it often smartly focused on proven biological markers like enlarged, hypochromatic nuclei for cancer, it sometimes made correct diagnoses by staring at random, non-traceable artifacts, raising severe trust issues for clinical deployment.

Key Takeaway: Deep learning models are currently fantastic, ultra-reliable screening assistants for binary benign/malignant triage, but they aren't ready to replace human pathologists for complex subtyping without massive, multi-institutional datasets. However, the AI's occasional focus on obscure visual data forces us to ask: is the machine just learning random artifacts, or has it successfully discovered subtle microscopic biological truths that human experts haven't even learned to see yet?

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Paper Discussed in this Episode:

A confidence-based, artificial intelligence pathology model for diagnosis of intrahepatic cholangiocarcinoma. Chang, Jay, Calderaro, et al. Annals of Oncology 2026. DOI: 10.1016/j.annonc.2026.02.018.

Episode Summary: In this journal club deep dive, we tackle one of the most frustrating diagnostic puzzles in liver cancer: differentiating primary intrahepatic cholangiocarcinoma (ICCA) from metastatic liver cancers. We examine a groundbreaking 2026 study introducing AI2CCA, a deep-learning pathology model that evaluates routine digitized slides. The study forces us to ask a critical question: how can we safely deploy AI in the clinic? The answer lies in teaching the machine to measure its own uncertainty, drastically reducing the need for invasive, exclusionary tests and accelerating life-saving treatments.

In This Episode, We Cover:

• The Ultimate Clinical Bottleneck: Understanding the high-stakes diagnostic overlap between ICCA and metastatic adenocarcinomas. Because these tumors look functionally identical—sharing irregular glandular structures, mucin secretion, and fibrotic responses—patients often face weeks of invasive endoscopies and body scans to rule out an occult primary site before targeted treatment can begin.

• The Foundation Model Bake-Off: Researchers pitted three advanced, self-supervised deep learning architectures against each other using retrospective data from 544 patients across five European centers: ◦ Ctranspath paired with HistoBistro. ◦ UNI paired with CLAM. ◦ CONCH paired with TITAN, which emerged as the winner by mapping gigapixels of tissue to pathology reports using multimodal visual-language training.

• The Secret Sauce - Predictive Entropy: An initial AUROC of 0.840 is not safe enough for clinical deployment. We break down how the team used Generalized-ODIN (G-ODIN) to calculate "predictive entropy"—a mathematical measurement of the AI's internal confusion when tissue is highly ambiguous.

• The Power of Saying "I Don't Know": By setting a strict confidence threshold and refusing to diagnose ambiguous slides, the AI2CCA model improved its AUROC to 0.958 and dropped its false positive rate to absolute zero. While it only retained 46% of cases for high-confidence predictions, it provides a safe "fast-track" that could essentially halve the clinical backlog for unnecessary gastrointestinal scopes.

• The Global Stress Test: To prove the AI didn't just memorize European lab stains, the team prospectively tested 161 new patients across France, India, and South Korea. Despite navigating completely different disease backgrounds—such as heavy cirrhosis and endemic liver flukes—the model achieved near-perfect accuracy (AUROCs of 1.00 and 0.965) with only one single misclassification globally.

Key Takeaway: True clinical AI doesn't need to replace the human diagnostic process; it just needs to know what it doesn't know. By perfectly triaging 46% of routine cases with zero false positives, AI2CCA transforms the human pathologist into the ultimate biological arbiter, freeing up their cognitive bandwidth for the most complex cases while allowing thousands of patients to skip unnecessary invasive tests

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Paper Discussed in this Episode:

Reporting checklist for foundation and large language models in medical research (REFINE): an international consensus guideline. Mese I, Akinci D’Antonoli T, Bluethgen C, et al. Diagn Interv Radiol 2026.

Episode Summary: In this special journal club edition of the digital pathology podcast, we tackle a massive structural problem in medical imaging and AI: the rapid adoption of foundation models and large language models (LLMs) that are completely outgrowing our traditional evaluation frameworks. We examine the groundbreaking 2026 REFINE consensus guideline that addresses the opaque and stochastic nature of generative AI, forcing researchers to fundamentally change how they report on these tools to move away from black-box unpredictability toward true reproducibility.

In This Episode, We Cover:

• The "Wooden Ruler" Problem: Traditional AI reporting tools, such as CLAIM and TRIPOD-AI, were built under the assumption that algorithms are deterministic, meaning they give the exact same output every time. Generative AI is inherently stochastic and sensitive to subtle variables, making old checklists function like rigid wooden rulers trying to measure a fluid target.

• The REFINE Framework: Created via a rigorous Delphi consensus process by 57 contributors from 17 countries, this robust 44-item, 6-section checklist is a massive global effort. It features a deliberate "N/A" filtering mechanism to practically accommodate highly diverse text, imaging, and multimodal study designs.

• Prompting is the New Coding: We explore why researchers must now treat prompt engineering with the exact same rigor as traditional source code. The guideline requires full transparency on prompting strategies, session memory policies, and precisely how patient clinical context (like BI-RADS or ICD codes) is integrated into the model.

• Corralling the Chaos (Stochasticity & The Human Element): Controlling an LLM requires detailing generation parameters like "temperature," which dictates model creativity. Crucially, studies must also document the prompt operator's characteristics, as a senior attending radiologist will intuitively guide a model very differently than a first-year resident, drastically skewing the output.

• The Contamination Crisis: We discuss the existential threat of dataset contamination, which occurs when an LLM has already memorized public test datasets (like MIMIC-CXR) during its pre-training phase. The guideline demands rigorous checks against the model's knowledge cut-off dates and full transparency regarding the use of synthetic data.

• Clinical Reality Check: A model's performance in a vacuum is meaningless if it cannot seamlessly integrate into a hospital's clinical workflow, such as its PACS. We detail why researchers must now explicitly outline clinical non-use cases, map out data privacy safeguards, and conduct formal failure analyses to categorize errors like hallucinations.

Key Takeaway: The REFINE guideline marks a critical maturation point for medical AI research. By rigorously addressing the unique chaotic elements of generative AI—such as prompt sensitivity, stochastic generation, and dataset contamination—this framework ensures that future medical AI studies provide a trustworthy, reproducible foundation of evidence that frontline clinicians can safely rely on for patient care

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Artificial Intelligence in Healthcare: From Diagnosis to Rehabilitation. Witek K, Nowocien M, Gerlach J, et al. Cureus 2026 Jan 25;18(1):e102286.

Episode Summary: In this journal club deep dive on the Digital Pathology Podcast, we completely bypass the venture capital hype and science fiction narratives to look strictly at the hard clinical evidence surrounding artificial intelligence in medicine. We examine a monumental 2026 narrative review synthesizing a full decade's worth of data across the entire healthcare continuum—from diagnosis to rehabilitation. We explore the proven clinical benefits, the structural limitations, and the highly unpredictable reality of integrating these advanced algorithms into live clinical workflows.

In This Episode, We Cover:

• The Diagnostic Powerhouse: Why AI truly shines in visually intensive specialties like radiology, ophthalmology, dermatology, and digital pathology. We also unpack the crucial bottleneck: why algorithms that achieve board-certified performance in "open book" retrospective lab settings often struggle when faced with the messy, artifact-heavy reality of a live clinic.

• Laboratory Medicine & LIS Optimization: How AI is functioning as a massive force multiplier behind the scenes. We discuss AI-driven lab test checkers that run continuous delta checks, acting as an algorithmic safeguard against inevitable human cognitive traps like anchoring bias during high-stress, 12-hour shifts.

• Physical Rehabilitation & Robotics: AI stepping out of the computer monitor and interacting directly with the physical world. We explore robotic hand exoskeletons that process real-time electromyiography data to adapt to stroke patients millisecond by millisecond, and the use of large language models (LLMs) to design personalized therapy programs. We also discuss why massive multi-center prospective validation is required before these become the standard of care.

• Conversational Agents (Chatbots): The delicate deployment of chatbots to bridge gaps in patient education and hold the line with immediate interventions for vulnerable individuals stuck on mental health waitlists. We emphasize why these agents must remain strictly as clinical adjuncts and triage tools, not replacements for empathetic human caregivers.

• The Four Pillars of Friction: The massive structural hurdles preventing immediate global deployment: generalizability and algorithmic bias, the "black box" of algorithmic transparency, infrastructure limitations, and the scramble by organizations like the FDA and EU to establish proper regulatory oversight.

Key Takeaway: The ultimate takeaway from a decade of data is that AI is a supportive clinical decision support technology, emphatically not a replacement for human healthcare professionals. The future of healthcare is the convergence of human and artificial intelligence; by letting algorithms absorb the heavy lifting of routine data verification, we may finally create the necessary breathing room to make clinical medicine profoundly human again

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Paper Discussed in this AI Journal Club: "Transforming Gastric Biopsy Diagnostics: Integrating Omics Technologies and Artificial Intelligence" by Nasar Alwahaibi, published in the journal Biomedicines.

Episode Summary: In this episode, we explore how traditional gastric biopsies are getting a massive, sci-fi-level upgrade. For over a century, diagnostic practice has relied heavily on visual pattern recognition via histomorphology—essentially looking at stained tissue under a brightfield microscope. Today, we discuss the paradigm shift toward data-driven "precision gastroenterology," made possible by merging high-resolution multi-omics technologies with the computational power of artificial intelligence (AI).

Key Topics Covered:

The Limits of the Status Quo: Traditional microscopic evaluation is foundational but limited. It suffers from interobserver variability (human disagreement), sampling limitations, and an inability to fully capture a tumor's biological complexity or predict how a disease will progress and respond to treatment.

The Multi-Omics Revolution: Moving beyond basic static genomics to include transcriptomics, epigenomics, proteomics, and metabolomics provides a comprehensive map of cellular activity—what we call the "active construction site". We highlight a pivotal study by Kamio et al., which demonstrated that knowing a patient's specific TP53 mutation profile (such as the R175H mutation) in early-onset gastric cancer can predict a significantly longer time-to-treatment failure (17.3 months vs. 7.0 months) using oxaliplatin chemotherapy.

AI as the Medical Co-Pilot: Deep learning models and convolutional neural networks (CNNs) are transforming both endoscopy and histopathology. For example, an AI-assisted tandem study showed a reduction in gastric neoplasm miss rates from 27.3% to an incredible 6.1%. Furthermore, AI tools have demonstrated the ability to outperform human experts in objectively scoring gastritis severity. However, it is crucial to remember that AI is currently a decision-support tool that still requires human oversight, especially in complex clinical realities.

The "Endo-Histo-Omics" Paradigm: We dive into the future of integrated diagnostics, such as the HTML (Highly Trustworthy Multi-omics Learning) framework. This self-adaptive model dynamically tailors its computational architecture to prioritize the most reliable data from a specific sample's unique multi-omics and visual profile.

Real-World Roadblocks: Before this becomes the standard of care at your local clinic, the medical field must overcome four main pillars of limitations: AI hurdles (data annotation burdens, black-box models), omics constraints (high costs, tiny biopsy sizes), integration complexity (lack of standardized software frameworks), and ethical/regulatory challenges (data privacy, algorithmic bias, and accountability).

Conclusion: The traditional intuition of the pathologist is evolving as we transition toward personalized, multi-omics management. Keep questioning the data, exploring the mechanics of the science, and we will see you on the next episode!

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Paper Discussed in this AI Journal Club:

From Image-Guided Surgery to Computer-Assisted Real-Time Diagnosis with Hyperspectral and Multispectral Imaging: A Systematic Review in Gynecologic Oncology. Innocenzi C, Pavone M, Seeliger B, et al. Diagnostics 2026.

Episode Summary:

In this journal club deep dive, we explore a groundbreaking 2026 systematic review that challenges the traditional intraoperative frozen section. We examine how hyperspectral and multispectral imaging are fundamentally reshaping the operating room by giving surgeons real-time, molecular-level vision. What happens when we can see beyond the visible spectrum, and how do we navigate the philosophical boundary between human surgical intuition and artificial intelligence?

In This Episode, We Cover:

• The End of the "Frozen Section" Waiting Game: Why current intraoperative pathology wastes precious surgical time and how "optical biopsies" provide cellular-level insight without the need for tissue contact, contrast agents, or freezing.

• The Science of the Spectral Fingerprint: Moving beyond standard RGB monitors that limit what surgeons can see. How malignant tissues interact with light—through refraction, scattering, absorption, and fluorescence—to create unique optical signatures that our naked eyes completely miss.

• Entering the Hypercube: How the 3D data sets of spectral imaging are captured: ◦ Spatial & Spectral Scanning: High-resolution methods that unfortunately struggle with breathing patients, making them susceptible to motion artifacts. ◦ Snapshot Technology: The real-time, video-rate method that balances spatial and spectral resolution for live clinical use.

• Clinical Showdowns - Cervical and Ovarian Cancer: ◦ Cervical Neoplasia: How multispectral imaging tracks the dynamic whitening of tissue following acetic acid application, plummeting false-diagnostic rates to an astonishing 1.7% compared to the 20-24% error rates of traditional methods. ◦ Ovarian Cancer: The massive hurdle of surgical blood acting as an "optical sink" that confuses sensors by causing spatial heterogeneity, and how mathematical normalization techniques correct these specific errors. ◦ The Falloposcope: A look at miniaturized technology safely navigating the fallopian tubes, combining optical coherence tomography (OCT) and multispectral imaging to detect early-stage cancers right where they originate.

• The "Black Box" and Spurious Correlations: Why feeding complex hypercube data into AI models (like CNNs and Random Forests) can be dangerous if the data sets are unbalanced. If an algorithm learns to diagnose cancer based on a spurious correlation like the glare of an OR light rather than actual biomolecular tumor markers, it will fail in new environments. We discuss the absolute necessity of Explainable AI (XAI) so surgeons can trust the biological plausibility of the machine's decisions.

Key Takeaway: The integration of hyperspectral and multispectral imaging serves as a real-time optical biopsy, offering incredible sensitivity for detecting malignancies. By pairing these tools with transparent, explainable AI, we are standing on the precipice of a new era that will drastically improve patient outcomes and force us to redefine the future of surgical intuition

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If AI can detect patterns we cannot see, how do we know when its answers are clinically trustworthy?

In this episode of DigiPath Digest #39, I explore a big-picture question in digital pathology and medical AI. Many models now match or even exceed human performance in specific diagnostic tasks. But most of that evidence comes from controlled or retrospective datasets. So what happens when we try to bring these tools into real clinical workflows?

I review four recent papers that help frame this challenge and point toward the next steps for trustworthy AI in healthcare.

You will hear about the role of prospective validation, real-world effectiveness, transparent reporting standards, and multimodal data integration as recurring themes across these studies.

Key Highlights

00:00 – Introduction
What do we do when AI detects signals that humans cannot see? The core challenge is verifying those outputs before trusting them in clinical decision making.

03:32 – AI Across the Healthcare Continuum
A narrative review shows AI achieving clinician-level performance in well-defined imaging tasks, including digital pathology. But most evidence comes from retrospective or controlled environments, and prospective validation remains limited.

08:34 – Multi-Omics and AI in Gastric Biopsy Diagnostics
Morphology alone cannot fully capture molecular heterogeneity or predict disease progression. Integrating genomics, proteomics, metabolomics, and other omics with AI is shifting gastric pathology toward data-driven precision gastroenterology.

13:38 – Hyperspectral Imaging for Real-Time Surgical Guidance
Spectral imaging can analyze tissue composition during surgery without staining, freezing, or contact with the tissue. Studies show promising sensitivity for detecting malignancy and supporting intraoperative decision making.

17:20 – REFINE Reporting Guideline for Foundation Models and LLMs
An international consensus guideline introduces a 44-item reporting checklist to standardize how AI studies are described. The goal is transparent, reproducible, and comparable research in medical AI.

22:35 – Big Takeaway
AI should be viewed as clinical decision support, not a replacement for clinicians. Real-world validation, ethical governance, and reproducible research standards will determine how these tools enter pathology workflows.

References (Articles Discussed)

Artificial Intelligence in Healthcare: From Diagnosis to Rehabilitation
https://pubmed.ncbi.nlm.nih.gov/41755929/

Transforming Gastric Biopsy Diagnostics: Integrating Omics Technologies and Artificial Intelligence
https://pubmed.ncbi.nlm.nih.gov/41751306/

From Image-Guided Surgery to Computer-Assisted Real-Time Diagnosis with Hyperspectral and Multispectral Imaging
https://pubmed.ncbi.nlm.nih.gov/41750768/

REFINE Reporting Guideline for Foundation and Large Language Models in Medical Research
https://pubmed.ncbi.nlm.nih.gov/41762555/

If you enjoy staying current with digital pathology and AI research, this episode will help you connect the dots between promising algorithms and practical clinical adoption.

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Paper Discussed in this AI Journal Club:

Masry ME, Gnyawali S, Jacobson M, Xue Y, Sen C, Wachs J, Gordillo G. AutoMated Burn Diagnostic System for Healthcare (AMBUSH). Plast Reconstr Surg Glob Open. 2023 Oct 18;11(10 Suppl):128-129.

doi: 10.1097/01.GOX.0000992564.42240.e3. PMCID: PMC10566867.

Episode Summary: In this journal club deep dive, we tackle a clinical problem that has frustrated surgeons for decades: accurately diagnosing burn depth. We examine a groundbreaking study introducing AMBUSH-AI, an artificial intelligence system that evaluates ultrasound imaging to outperform the diagnostic accuracy of human experts. When the stakes are a lifetime of severe scarring from under-treatment versus the painful trauma of an unnecessary skin graft, can a combination of standard ultrasound and AI completely eliminate the dangerous guesswork of human visual inspection?.

In This Episode, We Cover:

• The Diagnostic "Coin Toss": Why distinguishing between deep partial and full-thickness (third-degree) burns is the ultimate clinical challenge. We discuss the terrifying reality that experienced burn surgeons only achieve about 76% accuracy in visual assessments, while non-experts sit at 50%—literally a coin toss.

• The Two-Part Tech Combo (Anatomy and Stiffness): ◦ B-Mode Ultrasound: The standard imaging modality that provides the anatomical landmarks, letting the machine know exactly where the epidermis, dermis, and hypodermis are located. ◦ Tissue Doppler Elastography Imaging (TDI): The secret sauce that measures tissue stiffness. When skin burns, structural proteins denature and tangle, making the tissue physically stiffer. TDI visualizes this stiffness with color overlays—red for healthy and supple, blue for stiff and burned.

• Finding the Ground Truth: Why you can't calibrate a new, precise tool against a broken ruler. Instead of comparing the AI to flawed human visual estimates, the researchers validated the AI against actual tissue biopsies taken in the operating room, establishing an undeniable histological reality.

• The Results - Outperforming the Experts: In the human clinical trial, AMBUSH-AI achieved a staggering 95% overall accuracy. Crucially, it had a 100% sensitivity rate for surgical cases, meaning it did not miss a single patient who definitively needed an operation to prevent severe morbidity.

• The AI's "Glass Box" Design: Why surgeons will never trust a mysterious "black box" that just spits out a diagnosis. AMBUSH-AI is designed as an explainable model; it outputs plain text explaining its reasoning (e.g., "dominant, continuous blue pattern is present in the hypodermis"), acting as a transparent decision-support tool rather than a robot replacement.

• The Future of Triage: How this technology could be paired with Point of Care Ultrasound (POCUS) on portable tablets in military combat zones and rural ERs, giving any medic or general doctor the diagnostic confidence of a 20-year burn specialist.

Key Takeaway: The era of subjective, visual wound assessment is ending. By successfully translating the physical stiffness of a burn into objective, AI-interpreted data—a process called "computational palpation"—we can dramatically improve triage accuracy, save vital hospital resources, and spare patients from both dangerous under-treatment and unnecessary surgical trauma.

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Paper Discussed in this AI Journal Club:

Benchmarking large language model-based agent systems for clinical decision tasks. Liu, Y., Carrero, Z.I., Jiang, X. et al. npj Digit. Med. 2026.

Episode Summary: In this episode, we dive into a comprehensive 2026 benchmarking study that tests whether the highly hyped "Agentic AI" systems are truly ready to revolutionize clinical decision-making. We pit baseline large language models (LLMs) against complex, multi-agent systems in a series of rigorous medical exams and simulated doctor-patient dialogues. The big question: Do the autonomous planning and tool-use capabilities of AI agents actually translate to better diagnostic outcomes, or do they just add unnecessary computational bloat to the clinical workflow?

In This Episode, We Cover:

The Contenders - Baseline LLMs vs. AI Agents: Understanding the difference between a standalone LLM (like GPT-4.1, Qwen-3, or Llama-4) and "Agentic AI" systems (like Manus and OpenManus). Unlike simple chatbots, these agent systems are designed to autonomously reason, plan, and invoke external tools like web browsers, code executors, and text editors to solve complex clinical problems.

The Clinical Gauntlet: How researchers tested these models across three grueling healthcare benchmarks: AgentClinic (step-by-step simulated diagnostic dialogues), MedAgentsBench (a knowledge-intensive medical Q&A dataset), and Humanity’s Last Exam (highly complex, multimodal medical questions designed to defeat AI shortcut cues).

The Verdict - Modest Gains: The surprising reality that despite their advanced, multi-step toolsets, agent systems only yielded a modest accuracy boost over baseline LLMs. We discuss how customized agent models peaked at 60.3% accuracy on AgentClinic MedQA, 30.3% on MedAgentsBench, and struggled at a mere 8.6% on the text-only Humanity's Last Exam.

The Computational Price Tag: Why deploying these agents in a real hospital setting might be completely impractical right now. We discuss the massive inefficiency of these systems, noting that agents like OpenManus consumed more than 10 times the tokens and required more than double the response time compared to a standard LLM.

The Hallucination Problem: Exploring the persistent and dangerous issue of AI "making things up," such as inventing patient statements or assuming test results without asking the patient. We look at how researchers used targeted prompt engineering and an LLM-based output filter to successfully block 89.9% of these clinical hallucinations, though the core problem remains prevalent.

Key Takeaway: While Agentic AI systems show promise by autonomously gathering data and using external tools, their modest accuracy improvements are currently overshadowed by massive computational demands, increased response times, and persistent hallucinations. They represent a step forward in clinical AI architecture, but they remain too inefficient and unrefined for the fast-paced, high-stakes reality of routine clinical deployment.

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Paper Discussed in this AI Journal Club:

Wienholt, P., Caselitz, S., Siepmann, R. et al. Hallucination filtering in radiology vision-language models using discrete semantic entropy. Eur Radiol (2026). https://doi.org/10.1007/s00330-026-12384-z

Episode Summary: In this deep dive, we strip away the marketing hype surrounding medical AI and confront the "black box" problem of Vision Language Models (VLMs) like GPT-4o. We examine a groundbreaking 2026 study published in European Radiology that tackles a terrifying clinical issue: these AI models are incredibly confident, articulate, and often completely wrong. We explore a clever new mathematical wrapper designed to catch the AI in a lie, forcing us to ask: how do we stop the AI from hallucinating with dangerous authority, and can we actually teach it to say "I don't know"?

In This Episode, We Cover:

• The Confident Liar Problem (The Baseline): Why generalist VLMs are fundamentally different from traditional, narrow medical AI. They are probabilistic engines designed to predict the next word, resulting in a dangerous baseline accuracy of just 51.7% on real-world clinical data—essentially a coin flip.

• The Mathematical Lie Detector (Discrete Semantic Entropy): How turning up the AI's "temperature" to 1.0 and asking the exact same question 15 times forces the model to brainstorm, revealing its hidden uncertainties.

• Semantic Clustering (Cutting through the Noise): If the AI says "pneumonia" and then "lung infection," human clinicians know it means the same thing. We discuss how the DSE algorithm groups these answers by their underlying clinical meaning to calculate whether the AI is confidently consistent (low entropy) or randomly guessing (high entropy).

• The Coverage Cost vs. Accuracy Trade-Off: The dramatic results of applying a strict DSE filter. GPT-4o's accuracy jumped from roughly 51% to over 76%, but with a massive catch—it remained completely silent on over half the cases, answering only 47.3% of the clinical questions.

• The Danger Zone (Where AI Fails): Breaking down the performance across modalities. While the AI shone at identifying organs and surprisingly excelled at angiography, it completely fell flat on abnormality detection. On complex 3D CT scans, the filter had to reject over 90% of the questions because the model was fundamentally confused.

• The Trap of the "Confident Hallucination": Why DSE measures consistency, not truth. We explore the nightmare scenario where an AI stubbornly hallucinates the exact same lie 15 times in a row, slipping past the safety filter and creating a massive risk for "automation bias" among clinicians.

• Clinical Feasibility: The surprising practicality of running 15 parallel queries in a real hospital workflow. Because they run simultaneously via an API, the safety check takes only 6 seconds and costs roughly $0.72 per question.

Key Takeaway: Building safer AI might paradoxically risk creating riskier doctors. While Discrete Semantic Entropy successfully filters out the AI's digital noise and confusion—transforming a failing model into a somewhat reliable, albeit very quiet, assistant—it leaves us with a critical human factors challenge. If the system flawlessly cherry-picks the easy cases and stays silent on the hard ones, we must ensure our own diagnostic muscles don't atrophy from over-trusting the machine.

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Paper Discussed in this Episode: A Deep Learning Framework for Automated Triage of Breast Cancer Biopsies in Malaysia: A Simulation Study to Reduce Resource Consumption and Diagnostic Turnaround Time. Susilo YKB, Yuliana D, Rahman SA, Leong SL. Clinical Breast Cancer 2026.

Episode Summary: In this journal club deep dive on the Digital Pathology Podcast, we explore a 2026 study tackling severe diagnostic bottlenecks in breast cancer care. Facing a critical shortage of pathologists and agonizing patient wait times, researchers in Malaysia designed a deep learning triage system. But here is the major twist: they trained their highly accurate AI entirely on fake, synthetic tissue. We examine how this virtual simulation could revolutionize resource-constrained healthcare systems and ask a profound philosophical question: are the most powerful medical tools of tomorrow going to be built from the digital ghosts of patients who never even existed?

In This Episode, We Cover:

• The FIFO Problem: Why the standard "First-In, First-Out" (FIFO) laboratory queue is failing patients, burying urgent malignancies under routine benign cases (which make up 70-80% of biopsies), and causing excruciating turnaround times of over 14 days.

• The AI Triage Solution: How researchers used a Convolutional Neural Network (based on ResNet50) combined with an attention-based Multiple Instance Learning (MIL) mechanism to analyze massive whole-slide images and automatically bump suspicious cases to the front of the line.

• Training on "Digital Ghosts": The wild reality of Generative Adversarial Networks (GANs) like StyleGAN2-ADA. To bypass privacy laws and data scarcity, the AI was trained on 10,000 completely synthetic biopsy slides that were mathematically so realistic, expert human pathologists gave them a plausibility rating of over 90%.

• The Virtual Hospital: How researchers built an in-silico Discrete-Event Simulation using a Python library called SimPy. By inputting real-world hospital parameters, they created a digital twin to safely stress-test their AI without risking real patient lives.

• Transformative Results: The simulation projected a 38.3% reduction in wait times for critical cancer cases and a massive 22.5% drop in pathologist workload (saving over 422 hours annually). It also highlighted a 15.2% decrease in toxic reagent use, proving AI can support green laboratory sustainability initiatives.

• The Reality Check: Why this incredible simulated blueprint still needs rigorous real-world clinical validation before it can overcome the physical, messy inconsistencies—like tissue folds, scanner downtime, and variable stains—of a live laboratory.

Key Takeaway: Algorithmic queue management can fundamentally transform resource-constrained health systems. By proving that a highly accurate, cancer-detecting AI can be trained on purely synthetic data, this study offers a compelling blueprint to bypass privacy hurdles and data scarcity, drastically cutting diagnostic delays and saving vital specialist hours

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Paper Discussed in this AI Journal Club:

Region-Based Segmentation of Lymph Node Metastases in Whole-Slide Images of Colorectal Cancer: A Pilot Clinical Study. Fayzullin A, Savelov N, Balkivskiy A, et al. Cancer Medicine 2026.

Episode Summary: In this deep dive, we strip away the marketing gloss of AI as a mere time-saving tool and look at its true value in the lab: saving lives through relentless vigilance. We examine a 2026 study on colorectal cancer that deploys a two-stage AI pipeline to hunt down microscopic lymph node metastases. By highlighting "Specimen 8"—a speck of cancer hidden within a busy, benign background—we explore why the real return on investment for AI in digital pathology isn't about speeding up the human, but acting as an automated safety net that catches what the human eye naturally misses.

In This Episode, We Cover:

• The 12-Node Burden: The grueling clinical reality of staging colorectal cancer, where pathologists must manually scan at least 12 regional lymph nodes for microscopic tumor cells—a perfect storm for change blindness and visual fatigue.

• The Mimics of Pathology: Why finding metastases isn't just looking for a "needle in a haystack," but fighting visual mimics like sinus histiocytosis that effortlessly camouflage tiny, poorly differentiated cancer cells.

• The Two-Stage AI Pipeline ("The Scout" and "The Artist"): ◦ The Scout (GoogLeNet): A lightweight classification model that acts as a binary filter, achieving a staggering 100% recall by scanning image tiles and successfully filtering out confusing artifacts like tissue folds. ◦ The Artist (DeepLabV3+): A heavy-duty semantic segmentation model that draws precise boundaries around viable tumor cells while intelligently ignoring necrosis and lakes of mucin.

• The Hardware Validation Test: How the researchers proved their AI's robustness by testing it across different hardware (Hamamatsu and Leica scanners) to avoid the "silent killer" of AI projects: domain shift from scanner variability.

• The "Specimen 8" Revelation: A breakdown of the crucial moment the AI caught a 0.14 mm by 0.06 mm metastasis hiding in a benign pattern. The AI didn't save the pathologists time here—it actually slowed them down to verify—but it prevented a catastrophic misdiagnosis.

• The Return on Investment (ROI) Myth: Why hospital administrators need to stop looking at AI strictly for turnaround time speed. The study proved overall time savings were essentially negligible (1-3 seconds per case), but the quality assurance and patient safety derived from catching missed cancers were priceless.

Key Takeaway: The true value of AI in pathology isn't in racing the clock; it's in absolute vigilance. By successfully highlighting microscopic metastatic mimics that cause human false-negatives, AI proves its worth not as a turbo-button for the lab, but as a tireless quality assurance partner that ensures accurate cancer staging and optimal patient outcomes.

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Clinical Artificial Intelligence in 2026. Accuracy, Education, and Guardrails

Artificial intelligence is evolving fast in medicine. But how accurate is it. And are we building it safely?

In this episode of DigiPath Digest, I review five new studies shaping digital pathology, radiology, burn diagnostics, and agent-based large language model systems. We discuss accuracy gains, hallucination filtering, education challenges, and why safeguards are essential before clinical deployment.

Clear. Practical. Evidence-based.

⏱ Topics & Timestamps

[00:02] Introduction
Weekly journal club on digital pathology and artificial intelligence.

[05:13] Hallucination Filtering in Radiology
Using Discrete Semantic Entropy to detect hallucination-prone responses in Vision Language Models.
Accuracy improved from 51.7 percent to 76.3 percent after filtering high-entropy answers.

[15:04] Artificial Intelligence in Pathology Training
Supervised use during residency.
Balancing artificial intelligence adoption with preservation of morphological analysis and critical thinking.

[20:12] Colorectal Cancer Lymph Node Detection
Two-stage classification and segmentation model in Whole Slide Imaging.
Recall 1.0. Specificity 0.935. Dice coefficient 0.818.
Artificial intelligence as a second opinion.

[25:04] Burn Depth Prediction with Artificial Intelligence
Tissue Doppler Elastography and Harmonic B-mode ultrasound combined with artificial intelligence.
90 to 95 percent accuracy in human subjects.

[31:20] Agent-Based Large Language Model Systems
OpenManus and Manus evaluated in clinical simulations.
Up to 60.3 percent accuracy. High computational cost.
89.9 percent of hallucinations filtered by safeguards.

[40:08] Patient Access to Pathology Images
Why viewing pathology slides can empower patients and improve communication.

Resources

1. https://pubmed.ncbi.nlm.nih.gov/41720937/
2. https://pubmed.ncbi.nlm.nih.gov/41720644/
3. https://pubmed.ncbi.nlm.nih.gov/41716065/
4. https://pubmed.ncbi.nlm.nih.gov/41709317/
5. https://pubmed.ncbi.nlm.nih.gov/41708802/

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What if one of the biggest sources of diagnostic variability in prostate cancer isn’t the pathologist—but the stain we’ve trusted for decades?

In this episode, I speak with Professor Ingid Carlbom, founder of CADESS.AI, about a different way to approach prostate cancer grading—by rethinking staining, segmentation, and AI decision support from the ground up. We explore why 30–40% interobserver variability persists in Gleason grading and how optimized stains combined with explainable AI can significantly reduce that uncertainty.

Ingrid shares her journey from applied mathematics and computer science into pathology, the skepticism she faced in 2008, and why CADESS.AI chose not to “optimize H&E,” but instead developed a Picrosirius red + hematoxylin stain designed specifically for computational pathology. We discuss how grading at the gland and cellular level improves reproducibility, why explainability matters for trust, and what it really takes to build both stain and software as a single diagnostic workflow.

This conversation challenges long-held assumptions—and asks whether improving data quality should come before building smarter algorithms.

Highlights:

• [00:00–01:08] The problem: 30–40% disagreement in prostate cancer grading
• [01:08–03:03] Ingrid’s path from applied math to digital pathology
• [03:03–04:58] Early skepticism toward AI in pathology and fear of replacement
• [04:58–08:56] Why H&E limits segmentation—and how a new stain changes that
• [10:55–15:09] Clinical testing: non-inferiority, AI assistance, and NCCN risk stratification
• [19:47–22:59] Explainable UI: color-coded glands and pathologist override
• [26:16–27:29] Why grading glands (not whole slides) reduces variability
• [38:09–41:47] Regulatory challenges of combined stain + AI devices
• [45:52–48:55] The future of optimized stains in routine pathology

Resources from This Episode

• CADESS.AI – Prostate cancer decision support system
• NCCN prostate cancer risk stratification guidelines

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Sometimes a paper comes out that’s so practical and relevant to what we do in digital pathology that I know we have to talk about it.

In this episode, I dive into “A Guide for the Deployment, Validation and Accreditation of Clinical Digital Pathology Tools” from Geneva University Hospital (HUG) — one of the most useful, real-world frameworks I’ve seen for bringing digital pathology tools safely into clinical practice.

If you’ve ever built an AI model and wondered, “Now what?”, this episode is for you.
Because building the model is often the easy part — deployment is where things get complex.

This guide breaks the process into four practical phases every lab can follow:

1️⃣ Pre-Development – Define your clinical need, project scope, and validation plan before writing a single line of code.
2️⃣ Development – Build and integrate the algorithm in a production-ready environment.
3️⃣ Validation & Hardening – Turn your research code into a reliable, secure, and compliant clinical tool.
4️⃣ Production & Monitoring – Keep the tool validated and performing consistently over time.

We also discuss what makes qualification, validation, and accreditation different — and why that order really matters.
You’ll hear about the multidisciplinary team behind these deployments, especially the deployment engineer (DE) — the technical linchpin who turns AI research into clinical reality.

I share the story of HUG’s H. pylori detection tool, which cut diagnostic time by 26% while maintaining a 0% false negative rate. The team’s secret? Careful planning, quality control, and continuous user feedback — not just great code.

Other highlights include:

• Why integration often takes longer than building the AI model itself
• How to avoid invalidating your validation data
• What continuous performance monitoring looks like in real labs
• And why every lab still needs to do local validation, even with proven tools

If you’re working on digital or computational pathology tools — or just want to understand how AI safely moves from research to routine diagnostics — this episode will give you a roadmap grounded in real experience.

🎧 Listen now to learn how to move from algorithm to accreditation, step by step.

And if you’re just getting started in digital pathology, I’d love to give you my free eBook, Digital Pathology One-on-One: All You Need to Know to Start and Continue Your Digital Pathology Journey.
You’ll find the link to download it in the show notes.

See you in the episode!

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Is AI in pathology actually improving diagnosis — or just adding complexity?

In DigiPath Digest #37, we reviewed four recent publications covering AI-based biomarker quantification in glioblastoma, real-world digital workflow integration in prostate cancer, multimodal AI combining histopathology and genomics, and patient perspectives on AI in cancer diagnostics.

This episode connects technical performance with something equally important: trust.

Episode Highlights

[00:02] Community & updates
Digital Pathology 101 free PDF, upcoming patient-focused book, and global attendance.

[04:07] AI-based image analysis in glioblastoma
AI showed strong consistency with pathologists when quantifying Ki-67, P53, and PHH3.
Significant biological correlations (Ki-67 ↔ PHH3, PHH3 ↔ P53) were detected by AI — not by manual assessment.
Takeaway: computational quantification improves precision.

[09:28] Real-world digital workflow + AI in prostate cancer (France)
AI-pathologist concordance:
• 93.2% (high probability cancer detection)
• 99.0% (low probability slides)
Gleason concordance: 76.6%
10% failure rate due to pre-analytical artifacts.
Takeaway: infrastructure and sample quality still matter.

[15:58] Multimodal AI (MARBIX framework)
Combines whole slide images + immunogenomic data in a shared latent space using binary “monograms.”
Performance in lung cancer: 85–89% vs 69–76% unimodal models.
Takeaway: integrated data improves case retrieval and similarity reasoning.

[22:13] AI-powered paper summary subscription introduced
Structured summaries for busy professionals who want more than abstracts.

[26:17] Patient roundtable on AI in pathology (Belgium)
Patients expect:
• Better accuracy
• Faster turnaround
• Stronger collaboration

Trust is high when:
• Algorithms use diverse datasets
• Pathologists retain final responsibility

Clinical validity mattered more than full algorithm transparency.
Privacy concerns focused more on insurer misuse than cloud transfer.

Key Takeaways

• AI improves biomarker precision in glioblastoma.
• Digital pathology implementation works — but pre-analytics can limit AI performance.
• Multimodal AI represents the next meaningful step in precision diagnostics.
• Patients are not afraid of AI — they want validation, oversight, and governance.
• Human–AI collaboration remains central.

If you’re working in digital pathology, computational pathology, or precision oncology, this episode connects evidence, implementation, and patient perspective.

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Source Material: This AI journal Club episode is based on the original article, "The patient matters: a roundtable discussion on pathology in the era of digitization and AI," authored by Frederik Deman, Heleen Lauwers, Glenn Broeckx, Roberto Salgado, and Amelie Dendooven (Virchows Archiv, 2026).

In this episode, we dive deep into a critical yet often overlooked aspect of medical technology: the patient's perspective on the rapid integration of Digital Pathology (DP) and Artificial Intelligence (AI) in oncological diagnostics. While AI and digital tools promise faster and more precise cancer diagnoses, patient viewpoints have historically been absent from the conversation. We unpack the insights from a dedicated roundtable discussion involving six Flemish cancer-patient advocates, revealing their hopes, practical expectations, and surprising opinions on AI in the lab.

Key Takeaways & Topics Covered:

• Breaking Down Physical Barriers: We explore how patients view digital pathology as a way to easily share high-resolution tissue images across institutions, allowing subspecialized pathologists to collaborate and provide rapid second opinions without the delays of shipping physical slides.

• Trust, Accuracy, and Human Oversight: Patients exhibit a high level of trust in AI to speed up workflows and extract more precise data from biopsies. However, they emphasize that trust is contingent on algorithms being trained on diverse, high-quality data and pathologists retaining ultimate oversight.

• The "Black Box" Debate: Do patients need to know exactly how AI makes its decisions? Surprisingly, no. Patients prioritize clinical validity and accuracy over full algorithmic transparency. If a tool is proven safe and reproducible, a lack of "explainability" is not seen as a barrier to its use, though they encourage scientists to keep studying how these models work.

• Redefining Privacy Fears: The advocates were largely unconcerned with their pseudonymized data being transferred to cloud-based AI platforms. Their overriding privacy fear was not hacking, but rather the potential for health data to be accessed by insurance providers, which could lead to denied coverage or discrimination.

• AI as a Patient Empowerment Tool: Looking ahead, patients strongly desire future AI applications that can translate jargon-heavy pathology reports into lay-friendly language. They also envision AI generating customized questions about prognostic implications or follow-up tests, allowing patients to have more active, focused consultations with their doctors.

• The Cost of Innovation: We discuss the financial tension of AI in healthcare. While some patients are willing to pay a modest premium for faster AI-assisted diagnoses, there is a strong consensus that linking advanced tech to out-of-pocket payments could exacerbate healthcare inequality.

• Clashing with the EU AI Act: We conclude with a fascinating contrast: the pragmatic stance of patients—who accept non-explainable AI as long as it works safely—appears to misalign with the strict transparency and interpretability requirements mandated by the upcoming EU AI Act.

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Paper Discussed in this AI Journal Club:

Artificial Intelligence-Based Digital Image Analysis for Assessing Ki67, P53, and PHH3 Expression in Glioblastoma Multiforme. Devrim T, Erkilinc G, Tuncer SS. J Coll Physicians Surg Pak 2026; 36(02):153-157

Episode Summary: In this journal club deep dive, we step out of the theoretical future of AI and look at a direct, hard-data showdown between artificial intelligence and the human eye. We examine a groundbreaking 2026 study on Glioblastoma Multiforme (GBM) that forces us to ask an uncomfortable question: What happens when the AI and the human completely disagree? And more importantly, is it possible that the AI is detecting a biological reality that experienced human pathologists are entirely missing?

In This Episode, We Cover:

• The "Boss Battle" of Neuro-Oncology: Understanding Glioblastoma Multiforme (GBM), the most aggressive primary brain tumor in adults, and why precise prognosis dictates the entire treatment strategy.

• The Big Three Biomarkers (The Speedometer, The Brakes, and The Neon Sign):

◦ Ki67: The "speedometer" of the tumor, marking active cell proliferation.

◦ p53: The "guardian of the genome," acting as the emergency brakes for damaged cells. In GBM, these brakes are often broken or mutated.

◦ PHH3: A specific "neon mitosis tracker" that lights up dividing cells, offering a cleaner alternative to traditional manual counting.

• The Showdown - Humans vs. AI: Two experienced pathologists go head-to-head with an AI digital image analysis system (QuantCentre module by 3DHISTECH) on 20 adult GBM cases, looking at both 1 mm² and 7 mm² tumor hotspots.

• Round 1 - The Shocking Lack of Concordance: The AI and human pathologists had practically zero statistical agreement (Cohen's Kappa) on the raw numbers. The human eye acts interpretively, filtering out background noise, while the AI calculates literal pixel intensity.

• Round 2 - The AI's "Aha!" Moment: Biologically, a high proliferation rate (Ki67) must correlate with high mitosis (PHH3). Human pathologists failed to find any statistically significant link between these markers. The AI, however, found strong, biologically accurate correlations between Ki67 and PHH3, and between PHH3 and p53.

• The Future of the Lab: Why AI shouldn't replace pathologists, but rather serve as a hyper-sensitive tool to uncover hidden data patterns and personalize medicine. We also discuss the major roadblock preventing immediate clinical rollout: color standardization and image quality.

Key Takeaway: The lack of agreement between humans and machines doesn't mean the AI is wrong. By successfully identifying crucial biological relationships that humans missed due to attentional fatigue and subjectivity, the AI proved its data might actually be closer to the biological truth than our current gold standard.

Question of the Week for Our Trailblazers: Should we stop asking if the AI is as good as the human, and start asking if the human is actually precise enough to judge the AI? Let us know your thoughts!

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Paper Discussed in this AI Journal Club: Multimodal learning for scalable representation of high-dimensional medical data. Alsaafin A, Shafique A, Alfasly S, Kalari KR and Tizhoosh HR (2026). Front. Digit. Health 7:1709277. doi: 10.3389/fdgth.2025.1709277

Episode Overview In this episode, we tackle the infrastructure challenge in digital diagnostics: how do we efficiently store, search, and integrate the overwhelming amount of multimodal data generated by modern medicine? We take a deep dive into a groundbreaking paper from the Kimia Lab at Mayo Clinic that proposes an audacious solution. Learn how researchers are compressing gigapixel whole slide images and complex immune receptor sequences into a tiny, searchable 64-bit binary barcode (a "monogram") to power the next generation of case-based reasoning in oncology.

Key Topics Discussed

• The Intrinsic Heterogeneity Problem: Pathologists and computational biologists currently face a "silo" problem where visual whole slide images (WSIs) and textual immunogenomic data (T-cell and B-cell receptor sequences) exist in completely different computational worlds. Integrating them is like comparing a satellite photo of a city to a book of poetry written in that city.

• Late vs. Early Fusion: Standard "late fusion" models are computationally heavy because they run two full, distinct pipelines, while "early fusion" often leads to the curse of dimensionality, creating huge continuous vectors that are impossible to search through in real-time.

• Introducing MarbliX: We break down Multimodal Association and Retrieval with Binary Latent Indexed matriX (MarbliX), a framework designed to compress gigabytes of multimodal data into an 8x8 binary barcode.

• Under the Hood of MarbliX (The 3 Phases):

◦ Phase 1 (Unimodal Transformation): The image data is prepped using SPLICE to segment tissue and fed into a DINO ViT vision transformer, while the messy genomic sequences are harmonized using "Seqwash" and fed into a BERT natural language model. Both output 768-dimensional vectors.

◦ Phase 2 (Multimodal Latent Association): The AI plays a "translation game" using hybrid autoencoders. One network looks at the tissue image to predict the genetic sequence, and the other looks at the genetics to predict the tissue architecture. This forces the model to learn the shared biological signal connecting phenotype and genotype.

◦ Phase 3 (Binarization): Using triplet contrastive learning, the model organizes patients in a mathematical space so similar diseases cluster together, eventually squashing the data into just 64 zeros and ones.

• The Binary Trade-off & Hamming Distance: While binarization loses some precision compared to continuous floating-point math, it enables the use of "Hamming distance." This simple bitwise operation counts mismatches, allowing a database of 10 million patients to be searched in milliseconds on standard hardware.

• Real-World Results: Tested on TCGA datasets, the MarbliX multimodal approach showed a massive 15% jump in retrieval performance over using histopathology images alone, achieving 85% to 89% accuracy in distinguishing lung cancer subtypes.

• AI as a Librarian, Not a Judge: By retrieving the top 10 most similar historical cases based on barcode similarity, MarbliX empowers doctors with context and historical evidence rather than just giving a black-box diagnosis.

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What actually needs to be in place before digital pathology can replace the microscope?

In this episode of DigiPath Digest, I walk through the 2026 Polish Society of Pathologists guidelines and translate them into practical steps for real pathology labs. This isn’t theory. It’s about hardware fidelity, data integrity, validation, and AI integration — and what each of these actually requires in daily workflow.

We talk about scanner resolution standards (≤0.26 μm per pixel), 4K monitor calibration, visually lossless compression (20:1), scalable storage, pathologist-driven validation, and what “non-inferiority” truly means.

Digital pathology is not just a change of medium. It’s an operational shift.

Episode Highlights

[00:02] Community & growth
1,600+ new newsletter subscribers, 10,000+ Facebook members, and free Digital Pathology 101 book access.

[07:20] The 4 pillars of adoption
Hardware fidelity · Data integrity · Clinical validation · Future integration.

[08:30] Hardware requirements
40x equivalent scanning (≤0.26 μm/px), 4K monitors, >300 cd/m² luminance, 10-bit color depth.

[12:00] Workflow & throughput
200–300 slides/day per scanner, automated focus control, urgent case prioritization.

[17:25] Storage & archiving
~1 GB per slide. Active archive (6–24 months). Long-term retention (10–20 years). GDPR compliance & TLS encryption.

[23:09] Validation philosophy
Pathologist-centered validation.
Two phases:
• Familiarization (~20 retrospective cases)
• Dual review with discrepancy tracking
Goal: digital must be non-inferior to glass.

[29:03] AI in digital pathology
AI supports quantification (Ki-67, HER2, ER/PR, PD-L1), tumor detection, and future multimodal predictions — but pathologists remain central.

[33:26] Intraoperative telepathology
<5-minute scan-to-view time.
Minimum 100 Mbps upload.
Redundancy and safety protocols required.

[34:50] Can digital cameras replace scanners?
Hybrid workflows exist. Regulatory compliance still applies.

[38:19] Adoption checklist summary
Certified scanners (CE-IVD/FDA), calibrated monitors, scalable storage, phased validation, and documented QC.

Key Takeaways

• Digital pathology adoption is a structured process — not just buying a scanner.
• Validation is individualized and tissue-specific.
• Infrastructure and quality control are as important as image quality.
• AI enhances reproducibility and quantification but does not replace pathologists.
• Regulatory compliance and data governance are non-negotiable.

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This AI Journal Club Episode is based on the following paper:

Szylberg Ł, Durślewicz J, Chmura Ł, Rezner W, Bartczak A, Marszałek A. Guidelines for the adoption of digital pathology in clinical pathology units recommended by the polish society of pathologists. Diagn Pathol. 2026 Jan 30;21(1):13. doi: 10.1186/s13000-026-01762-2. PMID: 41618426; PMCID: PMC12874716.

You can read it here: https://pubmed.ncbi.nlm.nih.gov/41618426/

You can view the YouTube version with captions here: [coming soon]

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This session is a practical walkthrough of where digital pathology and AI truly stand in early 2026—based on five recent PubMed papers and real-world implementation experience.

In this episode, I review new clinical adoption guidelines, AI applications in liver cancer imaging and pathology, AI-ready metadata for whole slide images, non-destructive tissue quality control from H&E slides, and machine learning–assisted IHC scoring in precision oncology.

This conversation is not about hype. It’s about standards, validation, data integrity, and clinical translation—the factors that decide whether AI tools stay in research or reach patient care.

Episode Highlights

• 01:21 – Practical digital pathology adoption guidelines (Polish Society of Pathologists)
• 08:05 – AI in liver cancer imaging & pathology, and why framework alignment matters
• 18:10 – AI-generated tissue maps as metadata for WSI archives
• 23:01 – PathQC: predicting RNA integrity and autolysis from H&E slides
• 32:14 – ML-assisted IHC scoring in genitourinary cancers
• 29:42 – Digital Pathology 101 book + community updates

Key Takeaways

• Digital pathology adoption still requires clear standards and validation workflows
• AI performs best when aligned with existing diagnostic frameworks (e.g., LI-RADS)
• Metadata extraction is a low-effort, high-impact AI use case
• Slide-based quality control can support biobanking and biomarker research
• Automated IHC scoring improves consistency—but adoption remains uneven globally

Resources Mentioned

• Digital Pathology 101 (free PDF & audiobook)

Publication Links: a. https://pubmed.ncbi.nlm.nih.gov/41618426/ b. https://pubmed.ncbi.nlm.nih.gov/41616271/ c. https://pubmed.ncbi.nlm.nih.gov/41610818/ d. https://pubmed.ncbi.nlm.nih.gov/41595938/ e. https://pubmed.ncbi.nlm.nih.gov/41590351/

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What happens when artificial intelligence moves beyond images and begins interpreting clinical notes, kidney biopsies, multimodal cancer data, and even healthcare costs?

In this episode, I open the year by exploring four recent studies that show how AI is expanding across the full spectrum of medical data. From Large Language Models (LLM) reading unstructured clinical text to computational pathology supporting rare kidney disease diagnosis, multimodal cancer prediction, and cost-effectiveness modeling in oncology, this session connects innovation with real-world clinical impact.

Across all discussions, one theme is clear: progress depends not just on performance, but on integration, validation, interpretability, and trust.

HIGHLIGHTS:

00:00–05:30 | Welcome & 2026 Outlook
New year reflections, global community check-in, and upcoming Digital Pathology Place initiatives.

05:30–16:00 | LLMs for Clinical Phenotyping
How GPT-4 and NLP automate phenotyping from free-text EHR notes in Crohn’s disease, reducing manual chart review while matching expert performance.

16:00–23:30 | AI Screening for Fabry Nephropathy
A computational pathology pipeline identifies foamy podocytes on renal biopsies and introduces a quantitative Zebra score to support nephropathologists.

23:30–29:30 | Is AI Cost-Effective in Oncology?
A Markov model evaluates AI-based response prediction in locally advanced rectal cancer, highlighting when AI delivers value—and when it does not.

29:30–38:30 | LLM-Guided Arbitration in Multimodal AI
A multi-expert deep learning framework uses large language models to resolve disagreement between AI models, improving transparency and robustness.

38:30–44:30 | Real-World AI & Cautionary Notes
Ambient clinical scribing in practice, AI hallucinated citations, and why guardrails remain essential.

KEY TAKEAWAYS

• LLMs can extract meaningful clinical phenotypes from narrative notes at scale
• AI can support rare disease diagnosis without replacing expert judgment
• Economic value matters as much as technical performance
• Explainability and arbitration are becoming critical in multimodal AI systems
• Human oversight remains central to responsible adoption

Resources & References

• Digital Pathology Place: https://www.digitalpathologyplace.com
• Digital Pathology 101 (free PDF, updates included)
• Automating clinical phenotyping using natural language processing
• Zebra bodies recognition by artificial intelligence (ZEBRA): a computational tool for Fabry nephropathy
• Cost-effectiveness analysis of artificial intelligence (AI) for response prediction of neoadjuvant radio(chemo)therapy in locally advanced rectal cancer (LARC) in the Netherlands
• A multi-expert deep learning framework with LLM-guided arbitration for multimodal histopathology prediction

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What really changed in digital pathology this year—and what still needs work?

As we close out 2025 and step into 2026, I wanted to pause, reflect, and share what I’ve seen shift from theory to real-world practice across labs, conferences, and clinical workflows.

I look back at the most meaningful developments in digital pathology and AI in 2025—from wider adoption of primary diagnosis on digital slides to more grounded, evidence-driven use of AI tools. We’ve moved past hype and pilots and started asking harder questions about validation, workflow integration, regulation, and trust.

I also share what I believe matters most as we move into 2026: building real-world evidence, upskilling pathologists, and focusing on tools that genuinely support patient care rather than distract from it.

This episode is for anyone navigating change in pathology and wondering where to invest their time, energy, and curiosity next.

Episode Highlights:

• [00:00–02:10] Why 2025 marked a turning point for digital pathology adoption
• [02:10–05:40] From pilot projects to clinical workflows: what actually changed
• [05:40–08:30] How AI usage shifted toward triage, quantification, and decision support
• [08:30–11:45] Why validation and real-world evidence became central topics
• [11:45–14:20] The growing role of pathologists in AI governance and quality assurance
• [14:20–17:10] Lessons from conferences, labs, and conversations worldwide
• [17:10–20:00] What I expect to see more of in 2026—and what I hope we leave behind

Key Takeaways:

• Digital pathology is no longer experimental—it’s becoming routine in more labs.
• AI tools are shifting from novelty to practical clinical support.
• Validation, regulation, and workflow fit matter more than algorithm performance alone.
• Training and continuous learning are now essential career components for pathologists.
• 2026 will reward teams that test, measure, and iterate thoughtfully.

Resources Mentioned

• Digital Pathology Place – education, podcasts, and community
• 2025 CONFERENCE insights and real-world lab experiences

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What if the biggest breakthrough in pathology AI isn’t a new algorithm—but finally sharing the data we already have?

In this episode, I’m joined by Jeroen van der Laak and Julie Boisclair from the IMI BigPicture consortium, a European public-private initiative building one of the world’s largest digital pathology image repositories. The goal isn’t to create a single AI model—but to enable thousands by making high-quality, legally compliant data accessible at scale.

We unpack what it really takes to build a 3-million-slide repository across 44 partners, why GDPR and data-sharing agreements delayed progress by 18 months, and how sustainability, trust, and collaboration are just as critical as technology. This conversation is about the unglamorous—but essential—work of building infrastructure that will shape pathology AI for decades.

⏱️ Highlights with Timestamps

• [00:00–01:40] Why BigPicture focuses on data—not algorithms
• [01:40–03:16] Scope of the project: 44 partners, 15–18 countries, 3M images
• [03:16–06:20] The 18-month delay caused by legal frameworks and GDPR
• [06:20–11:52] Extracting data from heterogeneous lab infrastructures
• [11:52–13:38] Current status: 115,000 slides uploaded and growing
• [13:38–18:39] Why LLMs and foundation models make curated data more valuable than ever
• [18:39–23:49] Industry collaboration and shared negotiating power
• [23:49–28:06] Data access models and governance after project independence
• [28:06–31:59] Sustainability plans and nonprofit foundation model
• [37:02–43:18] Tools developed: DICOMizer, artifact detection AI, image registration

📚 Resources from This Episode

• IMI BigPicture Consortium
• GDPR & Data Sharing Agreements (DSA)
• DICOMizer & SEND metadata tools
• Artifact detection AI for slide QC
• European AI Factories initiative

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What if the biggest transformation in digital pathology this year had nothing to do with new hardware—and everything to do with how we think about value, workflow, and readiness?

In this year-end recap livestream from the 11th Digital Pathology & AI Congress in London, I break down what truly mattered in 2025. Instead of focusing on buzzwords or hype cycles, this episode highlights the practical advances shaping diagnostics, patient care, and drug development—and the mindset shift our field must embrace to move forward.

Digital pathology is no longer “early adoption.” It’s becoming essential infrastructure. And yet the biggest barrier isn’t scanners or algorithms—it’s the knowledge and confidence needed to use them well.

Key Highlights & Timestamps

0:00 — Setting the Stage from London

An overview of the forces that shaped digital pathology in 2025: workflow integration, clinical readiness, and the move from theory to operational reality.

1:45 — Leica’s Expanded Portfolio & FDA-Cleared Collaborations

A look at Leica’s updated scanner lineup and co-developed, FDA-cleared solutions with Indicollabs. These launches reflect a broader industry trend toward highly specialized, clinically validated digital tools designed for end-to-end workflows.

4:12 — The Acceleration of Companion Diagnostics

From Artera’s de novo–approved prostate prognostic test to AstraZeneca’s TROP2 scoring efforts, 2025 pushed computational pathology directly into therapeutic decision-making.

6:20 — Why Workflow Integration Became the Theme of 2025

Partnerships like BioCare + Hamamatsu + Visgen and Zeiss + MindPeak show where the field is heading: full-stack solutions, not isolated tools. Labs want interoperability, reliability, and simplified digital workflows.

9:10 — Adoption Challenges: ROI, Education & AI Uncertainty

We explore the realities slowing digital transformation:
– ROI is real, but requires workflow change
– AI anxiety persists among clinicians and patients
– Education is still the strongest driver of adoption

12:00 — 2025’s Innovation Highlights

Breakthroughs shaping the next phase of digital pathology include:
– emerging agentic AI platforms
– voice-enabled image management systems
– improved multiplexing technologies like Hamamatsu’s Moxiplex

15:40 — The Growing Intersection of Pathology & Genomics

AI models predicting genomic alterations from H&E images gained traction, especially for cases with minimal tissue. Tempus acquiring Paige signals the deepening connection between digital workflows and molecular data.

18:30 — What 2026 Will Require

Priorities for the coming year include:
– building agentic AI solutions capable of real workflow orchestration
– strengthening validation and QC
– sharing real-world deployment case studies
– expanding training and hands-on learning

RESOURCES:

1. The Lucerne Toolbox 3: digital health and artificial intelligence to optimise the patient journey in early breast cancer-a multidisciplinary consensus

2. Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report

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Have you ever thought, “Digital pathology sounds amazing, but without a scanner, what’s the point of learning it now?”
If so, this episode will change how you see your role in the future of pathology.

In this talk, I challenge one of the most persistent myths in our field: the belief that you need expensive hardware before you can begin your digital pathology journey. Through personal experience and the remarkable story of another pathologist who started with even less, I show why knowledge—not infrastructure—is what truly opens doors.

Highlights and Key Themes

0:00 – The Limiting Belief

I open with the core misconception I hear from pathologists worldwide: “I need a scanner before I can start.” I explain why hesitation, not lack of equipment, is the real barrier—and why waiting for perfect conditions keeps many people stuck.

2:24 – My Early Digital Pathology Story

I describe my residency in 2013, when a single scanner was “off limits” to trainees. Faced with a research project requiring consistent cell counting, I improvised using a microscope camera and Microsoft Paint.
It wasn’t sophisticated, but it was digital, consistent, and reproducible.
This experience taught me a foundational lesson: if you can measure something, measure it; don’t rely on visual estimation.

7:01 – How This Led to My First Digital Pathology Job

That basic Paint-and-dots project became my gateway to working at Definiens (now part of AstraZeneca).
I wasn’t hired for computational expertise; I was hired because I understood tissue, biology, and the value of quantifying what we see. Working alongside image analysis scientists showed me the exponential power of combining tissue knowledge with computational tools.

10:03 – Dr. Talat Zehra’s Story

I share the inspiring journey of Dr. Talat Zehra from Karachi, Pakistan, who began with no access to scanners and only a microscope camera.
During COVID shutdowns, she taught herself the foundations of digital pathology, joined global organizations, conducted a nationwide survey, and contacted AI vendors for access to platforms.
After many rejections, one vendor offered a trial account. In just six weeks, she completed three AI projects using microscope camera images—each one published in a peer-reviewed journal.
Her story highlights a universal truth: starting with curiosity and persistence matters far more than having perfect tools.

14:14 – Two Paths After a Conference

I explain the difference between the “forgetting loop” and the “learning path.”
Many attendees leave inspired but slip back into routine. Others commit to one consistent learning habit—journal clubs, vendor webinars, DigiPath Digest sessions—and return a year later with clarity, confidence, and momentum. These individuals become the people others seek out for guidance in digital pathology.

18:04 – Where to Begin

You don’t need a scanner or an institutional budget to start. What you need is structured knowledge.
I introduce my book, Digital Pathology One on One, and encourage listeners to choose one learning habit to build on after the episode. The only wrong choice is choosing nothing.

19:06 – Final Message

Knowledge drives adoption, not infrastructure.
Scanners, AI tools, and computational platforms already exist. What’s missing are people who understand how to interpret tissue digitally, collaborate with computational teams, and bridge biology with technology.
You have enough to begin today.

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What happens when AI becomes powerful enough to diagnose—not just one disease, but entire fields of medicine at once?
In this episode of DigiPath Digest #33, I break down four new PubMed abstracts shaping the future of digital pathology, clinical AI integration, federated learning, and multidisciplinary cancer care. Across every study, one message is clear: AI is accelerating, but human oversight defines its safe adoption.

Below are the full timestamps, key insights, and referenced research to help you explore each topic more deeply.

TIMESTAMPS & HIGHLIGHTS

0:00 — Welcome & Opening Question
How far can AI safely scale across medicine—and where must humans stay in control?

4:10 — AI in Forensic Medicine: Accuracy Meets Ethical Limits

Based on a systematic review, we discuss:

• AI advances in personal identification, pathology, toxicology, radiology, anthropology.
• Benefits: reduced diagnostic error, faster case resolution.
• Challenges: data diversity gaps, limited validation, lack of ethical frameworks.
  📌 Source: PubMed abstract on AI in forensic disciplines

10:55 — Confocal Endomicroscopy + AI for Pancreatic Cysts

Researchers trained a deep model on 291,045 endomicroscopy frames to detect papillary and vascular structures in IPMNs:

• 70% faster review time
• More consistent structure identification
• A step toward scalable “optical biopsy” workflows
  📌 Source: IPMN / confocal endomicroscopy AI abstract

16:40 — Federated Learning in Computational Pathology

A comprehensive review of FL for:

• Tissue segmentation
• Whole-slide image classification
• Clinical outcome prediction
  Key takeaway: FL can match or outperform centralized training—without sharing patient data—yet still struggles with heterogeneity, interoperability, and standardization.
  📌 Source: Federated learning review

22:15 — The Lucerne Toolbox 3: A Digital Health Roadmap for Early Breast Cancer

A global consortium of 112 experts identified 15 high-impact knowledge gaps and proposed 13 trial designs to integrate AI across early breast cancer care:

• AI-based mammography screening
• Personalized screening strategies
• Digital knowledge databases
• AI-driven treatment optimization
• Digitally delivered follow-up & supportive care
  📌 Source: The Lucerne Toolbox 3 (Lancet Oncology)

28:50 — Big Picture: AI Expands What’s Possible—but Humans Define What’s Acceptable

We close with the essential takeaway echoed across all four publications:
AI is getting smarter, faster, and more integrated—but clinical responsibility, validation, transparency, and multidisciplinary alignment remain irreplaceable.

STUDIES DISCUSSED AI in Forensics — systematic review examining applications & ethical barriers1. Confocal Endomicroscopy + AI for IPMN — high-frame-volume model improving diagnostic precision 2. Federated Learning in Computational Pathology — decentralized collaboration without data sharing 3. Lucerne Toolbox 3 (Lancet Oncology) — global consensus roadmap for integrating digital health & AI across early breast cancer care

Each paper reinforces a dua

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Why does it take three years to deploy a digital pathology tool that only took three weeks to build? That’s the reality no one talks about—but every lab feels every time they deploy a new tool...

In this episode, I sit down with Andrew Janowczyk, Assistant Professor at Emory University and one of the leading voices in computational pathology, to unpack the practical, messy, real-world truth behind deploying, validating, and accrediting digital pathology tools in the clinic.

We walk through Andrew’s experience building and implementing an H. pylori detection algorithm at Geneva University Hospital—a project that exposed every hidden challenge in the transition from research to a clinical-grade tool.

From algorithmic hardening, multidisciplinary roles, usability studies, and ISO 15189 accreditation, to the constant tug-of-war between research ambition and clinical reality… this conversation is a roadmap for anyone building digital tools that actually need to work in practice.

Episode Highlights

• [00:00–04:20] Why multidisciplinary collaboration is the non-negotiable cornerstone of clinical digital pathology deployment
• [04:20–08:30] Real-world insight: The H. pylori detection tool and how it surfaces “top 20” likely regions for pathologist review
• [08:30–12:50] The painful truth: Algorithms take weeks to build—but years to deploy, validate, and accredit
• [12:50–17:40] Why curated research datasets fail in the real world (and how to fix it with unbiased data collection)
• [17:40–23:00] Algorithmic hardening: turning fragile research code into production-ready clinical software
• [23:00–28:10] Why every hospital is a snowflake: no standard workflows, no copy-paste deployments
• [28:10–33:00] The 12 validation and accreditation roles every lab needs to define (EP, DE, QE, IT, etc.)
• [33:00–38:15] Validation vs. accreditation—what they are, how they differ, and when each matters
• [38:15–43:40] Version locking, drift prevention, and why monitoring is as important as deployment
• [43:40–48:55] Deskilling concerns: how AI changes perception and what pathologists need before adoption
• [48:55–55:00] Usability testing: why naive users reveal the truth about your UI
• [55:00–61:00] Scaling to dozens of algorithms: bottlenecks, documentation, and the future of clinical digital pathology and AI workflows

Resources From This Episode

• Janowczyk & Ferrari: Guide to Deploying Clinical Digital Pathology Tools (discussed)
• Sectra Image Management System (IMS)
• Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study - PubMed
• Digital Pathology 101 (Aleksandra Zuraw)

Key Takeaways

• Algorithm creation is the easy part—deployment is the mountain.
• Clinical algorithms require multidisciplinary ownership across 12 institutional roles.
• Real-world data is messy—and that’s exactly why algorithms must be trained on it.
• No two hospitals are alike; every deployment requires local adaptation.
• Usability matters as much as accuracy—naive users expose real workflow constraints.
• Pathologists must maintain baseline competency to avoid AI-induced deskilling.

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Why are billions of people still invisible in genomic research—and what does that mean for the future of precision medicine?

In this episode, I sit down with Victor Angel Mosti, founder and CEO of Omica.Ai, for one of the most insightful conversations I’ve recorded about data equity and building ethical, community-centered AI.

Victor shares not only his personal cancer story but also the staggering truth: Hispanic and Latino populations make up less than 1% of genomic datasets. This underrepresentation isn’t just a data gap—it’s a clinical risk.

We dive into disparities between healthcare systems, the promise of digital pathology as a low-cost entry point, the dangers of “parachute science,” and how Victor is building a living, ethical, transparent biobank through Omica. AI—built for true precision medicine rooted in community trust.

Highlights with Timestamps

• [00:00–01:40] Personal cancer experiences and diagnostic uncertainty
• [01:40–06:50] Victor’s medical journey across Mexico and the U.S.
• [06:50–11:42] The digitization gap: empathy vs. tech
• [11:42–16:43] The “coffee diversity” metaphor for genomic diversity
• [16:43–19:34] Funding disparities & the biotech cold-start problem
• [19:34–25:44] Digital pathology as a gateway to precision medicine
• [25:44–31:44] Avoiding “parachute science” and building community-first research
• [31:44–36:05] The Nagoya Protocol and benefit-sharing
• [36:05–41:47] Omica.Ai’s work, goals, and clinical-embedded approach
• [41:47–49:36] Creating future-proof, embedded biobanks
• [49:36–53:35] Blockchain for transparency and patient trust
• [53:35–54:39] Victor’s call to action: collaborate, include, and stay human

Resources from This Episode

• Omica.Ai – Community-driven precision medicine platform
• Nagoya Protocol – Framework for equitable biological use

Key Insights

• Cancer is personal—even for experts
• <1% representation of Latino genomes threatens clinical accuracy
• Digital pathology + AI can leapfrog infrastructure gaps
• Ethical biobanking requires trust, transparency, and local benefit
• Avoiding “parachute science” is essential
• Genetic diversity drives discovery—but only if we capture it
• Blockchain + dynamic consent = future of patient-centered data

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How far can AI go in helping us diagnose disease—without losing the human judgment patients rely on?

In this episode, I break down four studies shaping the future of digital pathology, oncology, and neurology. From spatial biology updates at SITC to voice-based Alzheimer’s detection, deep learning for sarcoma prognosis, and new guidelines for safe AI deployment, this week’s digest highlights where AI is making a real impact—and where caution still matters.

Episode Highlights

1️⃣ SITC Trends & Spatial Biology (00:00 → 07:40)

I share key updates from SITC 2025, including the growing role of multiplex immunofluorescence (mIF) and the need for integrated staining-to-scanning workflows. I also preview new educational content and upcoming podcast guests in global AI research.

2️⃣ Digital Neuropathology & Alzheimer’s (07:40 → 13:01)

A major review confirms that digital neuropathology is now robust enough for large-scale Alzheimer’s studies—opening doors for computational tools to link histology with cognition.

3️⃣ Patient Safety in AI (13:01 → 19:56)

An Italian review underscores the foundations of trustworthy AI: dataset quality, transparency, oversight, and continuous validation. I discuss why “patient-centered AI” must remain our standard.

4️⃣ Voice Biomarkers for Cognitive Decline (19:56 → 26:43)

AI models analyzing short speech recordings are showing high accuracy for early Alzheimer’s detection. This could make future screening simple, noninvasive, and more accessible.

5️⃣ Deep Learning for Sarcoma Prognosis (34:06 → 35:59)

A multi-instance CNN outperforms FNCLCC grading by identifying prognostic patterns in tumor center and periphery regions, offering new insights into soft-tissue sarcoma biology.

Takeaways

• mIF is maturing quickly but needs standardized, end-to-end workflows.
• Digital neuropathology is ready for broader Alzheimer’s research.
• Safe AI requires multidisciplinary collaboration and rigorous validation.
• Voice biomarkers may become powerful tools for early cognitive assessment.
• Deep learning can refine prognosis and reveal hidden tumor patterns.

Resources

Hamamatsu (MoxiePlex) • Biocare Medical (ONCORE Pro X) • SITC Programs • Recent publications on AI biomarkers and computational pathology.

Thanks for listening—and for being part of this growing digital pathology community.

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If your pathology reports and other data could talk, what would they say about the future of precision medicine? The truth is, most labs already have the data—they’re just not having a conversation with it.

In this episode, I talk with Peter O’Toole, President and Chief Software Architect at mTuitive. We recorded live at Pathology Visions and are covering the power of structured data and how it’s redefining the future of pathology reporting, AI, and clinical decision support.

We explore how structured reporting evolved from checklists to intelligence, why data hygiene and workflow integration matter more than AI buzzwords, and how collaboration across companies like mTuitive is helping labs turn their reports into clinically actionable data.

Highlights with Timestamps

• [00:00–05:40] Data as the new currency in pathology — Why structured data is the foundation for clinical, research, and trial insights.
• [05:40–10:30] AI & Large Language Models (LLMs) — What AI can (and can’t) do when your data isn’t structured.
• [10:30–19:25] AI workflow integration & voice recognition — How AI and structured reporting work together inside the LIS and IMS.
• [19:25–25:27] Overcoming resistance — Why pathologists initially resisted structured reports and how perceptions are shifting globally.
• [25:27–29:53] Decision support & beyond cancer — Expanding structured data to liver, skin, and even mental health pathology.
• [29:53–34:15] Collaboration as the catalyst — How partnerships create seamless ecosystems for pathology data.
• [34:15–37:03] Demo: Synoptic reporting in action — Real-time staging, automation, and compliance made easy.

Resources from this Episode

• mTuitive website: https://mtuitive.com
• CAP Synoptic Reporting Protocols – Standardized templates for structured pathology reports.
• Pathology Visions Conference 2025 – Event where this discussion took place.

Key Takeaways

✅ Structured reporting transforms pathology data from static text into actionable intelligence.
✅ AI and LLMs complement structured data—but can’t replace its clinical readiness.
✅ Clean data in = clean data out—data hygiene defines AI reliability and efficiency.
✅ Workflow integration and user-friendly design drive real-world adoption.
✅ Structured data unlocks clinical trials access, research potential, and decision support tools.
✅ Collaboration is key to building the connected ecosystem pathology needs.

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Is your lab truly digitally ready—or just scanning slides?

That’s the question I unpack in this live discussion from Day 2 of SITC’s 40th Anniversary Meeting, joined by David Anderson (Biocare Medical) and Don Ariyakumar (Hamamatsu Photonics).

Together, we explore what digital readiness really means for multiplex immunofluorescence (mIF) and how to build reliable, reproducible workflows that scale from research to clinical settings.

What We Discuss

The Discovery Funnel
I open by situating mIF within the broader discovery funnel: researchers begin with hundreds of biomarkers, narrowing down to focused 4–10 marker panels where true clinical utility begins. But this only works if the lab is digitally prepared from the start—from slide prep to data capture.

Defining Digital Readiness
David Anderson reframes digital readiness as everything that happens before the scanner turns on:

• Reagent consistency
• Antibody optimization
• Automation
• Standardized protocols
  All these elements ensure that downstream AI and image analysis tools work on clean, reproducible data instead of “fixing” noise later.

The Pre-Analytical Foundation
Don Ariyakumar emphasizes that scanning can’t fix variability. If staining or section quality isn’t standardized, digitization simply amplifies inconsistencies. True readiness starts at the bench, not the monitor.

Integration Across Vendors
We also talk about how interoperability between stainers, scanners, and spatial biology software is becoming essential. A disconnected workflow—mixing manual, unaligned steps—adds variables that no algorithm can fully normalize.

Lessons from IHC’s Evolution
The team draws parallels between multiplex IF today and IHC’s early days: once complex, now routine. Multiplex IF promises even richer tumor microenvironment insights, but only if standardization and automation catch up to the technology.

Beyond the Funnel
I revisit the “funnel” metaphor in a new light—arguing that as precision medicine grows, the bottom of the funnel broadens, not narrows. That means more tailored, smaller panels rather than one-size-fits-all assays, and a growing need for efficient, reproducible digital workflows.

Key Takeaways

• “Digital readiness” starts before scanning — with chemistry, automation, and process control.
• Consistent pre-analytical quality = reproducible, AI-ready data.
• Interoperability between systems (like Biocare’s ONCORE Pro X and Hamamatsu’s MoxiePlex) accelerates workflow standardization.
• Multiplex IF is maturing quickly, just as IHC once did—on its way to becoming a cornerstone of precision pathology.

Resources Mentioned

🔹 Biocare Medical (Booth 717) — ONCORE Pro X™ open slide stainer automating mIF, IHC, FISH, and ISH protocols.
🌐 biocare.net

🔹 Hamamatsu Photonics (Booth 415) — MoxiePlex™ multispectral imaging platform for high-plex spatial analysis.
🌐 hamamatsu.com

🔹 Society for Immunotherapy of Cancer (SITC) — 40th Anniversary Meeting information and programs.
🌐 sitcancer.org

Timestamp Highlights

00:00 — Welcome from SITC

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Can spatial biology and multiplex immunofluorescence truly transform how we understand cancer?

I went live from the Society for Immunotherapy of Cancer (SITC) 2025 — the 40th Anniversary Meeting to explore how spatial biology, multiplex IF, and digital pathology are coming together to redefine cancer diagnostics, research, and precision medicine.

This session kicked off a weekend of cutting-edge discussions with leaders from Hamamatsu (Booth 415) and Biocare Medical (Booth 717) — two companies helping laboratories around the world embrace digital transformation and spatial imaging in oncology.

🧠 Episode Highlights & Key Moments

0:00 — Introduction
I set the stage live from SITC 2025, explaining the goal of this series: to connect the science of multiplex imaging and spatial analysis with the practical needs of today’s cancer pathologists and researchers.

~1:00 — What Is Multiplex Immunofluorescence (IF)?
I explain how multiplex IF enables simultaneous detection of multiple biomarkers and immune cell types within a single tumor sample — giving us an unprecedented look at the tumor microenvironment and how cells interact.

~2:30 — The Spatial Biology Revolution
We talk about spatial biology as the “next frontier” beyond traditional histopathology — visualizing not just what is on the slide, but where it happens.

~5:00 — Digital Pathology & AI Readiness
I discuss the importance of digital pathology systems for slide digitization and how AI-powered software is now helping identify biomarkers, quantify expression, and accelerate immunotherapy research.

~7:30 — Featured Booths at SITC 2025

• Hamamatsu (Booth 415): High-end slide scanners and digital imaging solutions empowering pathology labs toward digital readiness.
• Biocare Medical (Booth 717): Showcasing the ONCORE Pro X — an open slide stainer that automates multiplex IF, IHC, FISH, and ISH protocols, plus smart software for optimizing complex staining processes.

~9:00 — Real-World Impact
We walk through clinical case examples where multiplex IF data guides immunotherapy decisions — helping clinicians stratify patients and tailor treatments more precisely.

~12:00 — Getting Started
I share practical advice for researchers ready to adopt spatial biology or digital pathology, from workflow design to validation and staff training.

~15:00 — Audience Q&A
Live questions from the audience on implementation, data integration, and scaling multiplex workflows across research and clinical environments.

~20:00 — Future Directions
We look ahead to how machine learning and spatial data integration will shape the next decade of immuno-oncology, including new SITC workshops on AI-driven tissue profiling.

~24:00 — Wrap-Up & Takeaways
Key message: spatial biology is not just a trend — it’s the next layer of precision medicine. I invite everyone to visit Hamamatsu (Booth 415) and Biocare (Booth 717) and to stay tuned for the next livestream focused on multiplex IF in clinical settings.

Resources Mentioned

🔹 Hamamatsu Photonics (Booth 415)
High-performance digital slide scanners and imaging systems.
🌐 hamamatsu.com

🔹 Biocare Medical (Booth 717)
ONCORE Pro X — Open sli

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Can one AI system learn from every organ — and teach us something new about all of them?

In this edition of DigiPath Digest #31, I explore how artificial intelligence is transforming pathology across multiple organ systems, revealing connections that help us diagnose faster, more consistently, and more accurately than ever before.

From glomerulonephritis to hepatocellular carcinoma, AI is no longer confined to a single specialty — it’s becoming the connective tissue between them.

What’s Inside:

1️⃣ AI for Bladder Cancer Classification
We begin with a multicenter study validating AI models for urothelial neoplasm classification using over 12,000 whole-slide images. Both CNNs and transformer models achieved high accuracy (AUC 0.983, F1 score 0.9). I discuss why the F1 score matters — and what it tells us about model balance between sensitivity and specificity.

2️⃣ AI in Colorectal Cancer Care
Next, we explore multimodal AI — integrating histopathology, radiology, genomics, and blood markers to modernize colorectal cancer workflows. AI now helps detect adenomas, infer microsatellite instability (MSI) from H&E slides, and predict treatment outcomes. I highlight the critical need for external validation, interpretability, and governance as AI enters clinical use.

3️⃣ AI for Glomerular Nephritis Diagnosis
A deep learning model trained on over 100,000 kidney biopsy images identified four nephritis types — FSGS, IgA, MN, and MCD — with over 85% accuracy. This technology could ease workloads and improve turnaround time in renal pathology. Still, I share why AI support may feel both empowering and unsettling for many pathologists.

4️⃣ AI in Liver Disease (MASLD & HCC)
AI is advancing noninvasive fibrosis staging and risk prediction in liver pathology. From large consortia like NIMBLE and LITMUS to predictive models for HCC therapy response, AI is moving us closer to precision hepatology. I also discuss the challenge of translating these tools from research to regulatory approval.

5️⃣ Lightweight AI for Domain Generalization
Finally, we look at one of pathology AI’s biggest challenges: domain shift — when a model trained on one scanner or staining style performs poorly elsewhere. The new Histolite framework shows how lightweight, self-supervised models can generalize across data sources — trading some accuracy for reliability in real-world use.

My Takeaway

Across every study, a single message stands out:
AI isn’t replacing pathologists — it’s amplifying our vision.
By connecting kidney, colon, liver, and bladder insights, AI is teaching us that medicine works best when it learns across boundaries.

Episode Highlights

• Bladder cancer AI validation (06:41)
• Multimodal colorectal AI (12:38)
• Glomerular nephritis deep learning (19:29)
• AI in liver pathology (29:55)
• Domain shift & Histolite framework (38:17)
• Halloween wrap-up + SITC preview (46:18)

Join me next time for updates from the SITC 2025 Conference, where I’ll be live at Booth 415 with Hamamatsu and Biocare, discussing how AI and spatial biology are converging to drive clinical utility.

DigitalPathology #AIinHealthcare #ComputationalPathology #CancerDiagnostics #LiverPathology #RenalPathology #FutureOfMedicine #DigiPathDigest

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If artificial intelligence can match—or even surpass—our diagnostic accuracy, what happens to the role of the pathologist?

That’s the question I explore in this episode of DigiPath Digest #30, where I break down three fascinating papers showing how AI is changing the way we diagnose, classify, and predict outcomes in renal transplant biopsies, thyroid cytology, and gastrointestinal cancers.

These studies don’t just prove AI’s potential—they reveal what it means for us, the humans behind the microscope.

Study 1 — Renal Transplant Biopsies: Precision in Every Pixel

A Japanese team examined how deep neural networks and large language models improve diagnostic consistency in renal transplant pathology.

They highlighted how the Banff Digital Pathology Working Group is retraining AI models alongside updated Banff classifications—creating a dynamic feedback loop between human expertise and machine learning.

In the U.S., over ten digital pathology systems are now FDA-cleared for primary diagnosis, showing that AI can support both accuracy and accountability. It’s not replacing us—it’s working with us.

Study 2 — Thyroid Cytology: From Overdiagnosis to Optimization

As someone who’s personally experienced thyroid cancer, this study hit close to home.

Researchers in China developed AI-TFNA, a multimodal system that combines whole-slide images and BRAF mutation data from over 20,000 thyroid fine-needle aspirations across seven centers.

The model achieved 93% accuracy, reducing unnecessary surgeries and improving clinical decisions. What’s especially impressive is Image Appearance Migration (IAM)—a technique that helps AI adapt across scanners and labs, ensuring reliable performance worldwide.

Study 3 — GI Cancer: Prognosis Reimagined

An international collaboration of over 2,400 patients introduced a Deep Learning Pathomics Signature (DLPS) that merges nuclear features, tumor microenvironment, and spatial single-cell data.

This AI-driven model predicted patient survival and therapy response more accurately than traditional TNM staging—even identifying which patients are most likely to benefit from chemotherapy or immunotherapy.

It’s precision medicine powered by pathology.

Reflections:

Each of these studies made me think about the balance between trust and technology. We’ve reached a point where AI can truly enhance diagnostic precision—but it also challenges us to stay actively engaged, curious, and informed.

Because the real risk isn’t that AI will outperform us—it’s that we’ll stop thinking critically once it does.

That’s why collaboration between pathologists, data scientists, and industry innovators matters more than ever.

AI isn’t replacing us—it’s redefining what excellence looks like in pathology.

DigitalPathology #AIinHealthcare #ComputationalPathology #RenalPathology #ThyroidCytology #CancerDiagnostics #DigiPathDigest

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Why do some pathologists still hesitate to trust digital slides—even after the FDA says “yes”? Because accuracy in digital pathology isn’t just about pixels—it’s about precision, validation, and confidence.

In this episode, I talk with Dr. Keith Wharton, MD, PhD, Global Medical Director at Roche Diagnostics, about how the Roche Digital Pathology DX system earned its FDA clearance for primary diagnosis—and what that means for the field.

We explore the science and strategy behind whole slide imaging (WSI) validation, the challenges of feature recognition, the meaning of non-inferiority, and the future of interoperability and AI in diagnostic systems.

If you’ve ever wondered what it takes to make a digital system clinically equivalent to the microscope—this episode is your roadmap.

🔹 Highlights with Timestamps

• [00:00–02:30] What “primary diagnosis” really means—and how the human brain processes histopathology features.
• [02:30–06:00] How Roche achieved FDA clearance for its DP200 and DP600 scanners—and why it’s “clearance,” not “approval.”
• [06:00–11:00] Breaking down the Roche Digital Pathology DX System components: scanner, viewer (Navify DP), and monitor.
• [11:00–16:00] Understanding the pixel pathway—the heart of system validation.
• [16:00–19:00] How the FDA defines precision and accuracy in validation studies.
• [19:00–30:00] Inside the massive multi-year validation studies: design, washout periods, and thousands of slide reads.
• [30:00–33:00] The non-inferiority margin (−4%)—why it matters and how Roche exceeded the benchmark.
• [39:00–45:00] The surprising “nuclear groove” discovery and what it reveals about how pathologists adapt to digital.
• [1:10:00–1:13:00] Future-ready systems and FDA flexibility through predetermined change control plans (PCCP).
• [1:25:00–1:35:00] Keith’s reflection: bridging the gap between discovery and clinical impact, and why the future of digital pathology is brighter than ever.

Resources from This Episode

• Roche Digital Pathology DX (DP200 & DP600) – FDA-cleared systems for primary diagnosis
• FDA Guidance (2016) – Technical performance standards for WSI
• American Journal of Clinical Pathology – Paper in press on validation study design
• Book: Chasing the Invisible by Dr. Thomas Grogan
• Frontiers Journal (2021) – Tissue Multiplex Analyte Detection in Anatomic Pathology (co-authored by Aleks and Keith)
• AJCP Paper
• Aleks and Keith’s Paper - Frontiers | Tissue Multiplex Analyte Detection in Anatomic Pathology – Pathways to Clinical Implementation

Key Takeaways

✅ FDA clearance requires rigorous demonstration of precision, accuracy, and statistical confidence.
✅ Non-inferiority margins (typically −4%) define the threshold for clinical equivalence to microscopy.
✅ Feature recognition in digital environments (like nuclear grooves) challenges perception and training.
✅ Interoperability and predetermined change control plans (PCCP) may accelerate system evolution.
✅ Digital pathology’s foundation is the pixel pathway—where scanner, viewer, and monitor all align.
✅ The field’s future depends on bridging discovery and practice, guided by robust validation.

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Live from Pathology Visions 2025 in San Diego, I share highlights from Day 2 of the world’s leading digital pathology conference, where experts explored how AI, empathy, and training are shaping the next generation of pathologists.

This episode captures the shift from technology as a tool to technology as a bridge — helping us connect with patients in more meaningful ways.

What I Talk About

1️⃣ From Pixels to Patients
We’ve built the infrastructure; now it’s about applying it. Pathology is no longer just digital — it’s personal, accessible, and human-centered.

2️⃣ Dr. Leah Lijah Joseph’s Keynote — Pathologists as Patients
Dr. Joseph, a cancer pathologist and survivor, shared her journey from diagnosing others to understanding her own slides. She now runs a patient pathology clinic, empowering people to see and learn from their own tissue samples.

3️⃣ The Power of Visualization
Dr. Joseph described how visualization and mental imagery support healing — a reminder that empathy and imagination can coexist with precision science.

4️⃣ AI & Imaging Innovation
From Google Research’s JPEG AXL format reducing file size by 30%, to discussions on color fidelity with DICOM’s David Clooney, we explored how innovation and accuracy must move hand-in-hand.

5️⃣ Cytology Goes Digital
With Hologic’s Genius Digital Diagnostic and AIXMed’s AI-assisted QC, cytology is entering a new era — faster, more accurate, and fully traceable through 100% AI quality control.

6️⃣ The Human Side of AI
I also share a personal story about my mother’s medical experience — and how even with all the tech, empathy remains the missing link. AI can’t replace compassion, but it can help us focus on it by automating what takes time away from patients.

Key Takeaways

• AI is enhancing accuracy and accessibility in diagnostics.
• Pathologists are taking on more patient-facing roles.
• Cytology digitization is revolutionizing quality and speed.
• Innovation must balance efficiency with color and data integrity.
• Empathy and communication will always define great medicine.

I hope this episode helps you see how AI, empathy, and education are shaping the next era of diagnostics.

Let’s continue building the bridge from pixels to patients, one slide at a time. 💡

PathVision25

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Live from Pathology Visions 2025 in beautiful San Diego, I sat down with Imogen Fitt from Signify Research to explore how AI, digital pathology, and interoperability are transforming the way we diagnose cancer and deliver patient care.

The conference theme, “From Pixels to Patients,” perfectly captures this year’s shift — from theoretical discussions about AI to real-world implementation and measurable outcomes.

We’re no longer just asking “what can AI do?” — we’re seeing how it’s actually improving accuracy, reducing barriers, and connecting pathologists and labs worldwide.

What We Discuss

1️⃣ From Hype to Application
This year, the buzz wasn’t about AI’s potential — it was about how it’s being used. We highlight case studies showing how digital tools are reducing diagnostic errors, improving collaboration, and even helping smaller labs digitize faster and more affordably.

2️⃣ PathPresenter’s Expanding Role
We dive into PathPresenter’s innovative model that gives users access to digital pathology at no initial cost, opening the door for over 75,000 professionals across 62 institutions. I share why I personally use PathPresenter for teaching and how it’s helping lower the barrier to entry for education, consultations, and patient care.

3️⃣ New Scanning Technology and Accessibility
We talk about compact scanners like Grundium’s four-slide scanner and new miniature models that make digitization possible even in smaller labs. The message is clear: you don’t need a massive system to start going digital.

4️⃣ Collaboration and AI in Action
Imogen shares updates from across Europe and Asia, including how hospitals are tackling storage, AI regulation, and workflow efficiency. We discuss emerging partnerships—Fujifilm, Voicebrook, Dolby, and others—that are making voice dictation, chat agents, and real-time AI insights part of the modern pathology cockpit.

5️⃣ The Human Side of AI Adoption
We also reflect on how digital pathology is changing careers and training. Younger pathologists expect digital tools as part of their workflow — and many won’t settle for less. We discuss how this new generation is driving adoption and pushing institutions to modernize.

My Reflections

I still remember when digital pathology felt intimidating — when only a few people were “allowed” to touch the scanner. But today, that’s changed completely.

Now, we’re living in an era where AI and digital pathology are not optional — they’re essential. The technology has matured, and so has the mindset around it. What excites me most is seeing how collaboration and accessibility are becoming central to innovation.

Key Takeaways

• AI in pathology is moving from hype to practice — focused on improving patient outcomes.
• Accessibility matters: smaller, affordable scanners and open platforms are democratizing digital pathology.
• Collaboration between vendors, clinicians, and technologists is key to faster, smoother adoption.
• The next generation of pathologists expects — and demands — a digital-first workflow.

Listen Now to Learn:

• How AI is reshaping cancer diagnosis
• The tools driving real change in labs today
• How collaboration fuels digital transformation in pathology

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Will AI make doctors and specialists less skilled—or even replace them?

That’s the question I explore in this episode of DigiPath Digest #29. As someone working where AI meets digital pathology, I’m both excited and cautious about how automation shapes our skills and professional identity.

In this episode, I discuss two studies that ask tough questions about AI, expertise, and the future of medicine.

What I Talk About:

1️⃣ Endoscopist Deskilling After AI Exposure (Lancet, 2025)
A multicenter Polish study found that after frequent AI-assisted colonoscopy use, endoscopists’ adenoma detection rate dropped by ~6% when performing procedures without AI. It suggests overreliance on automation can subtly dull vigilance.

It reminded me of how we depend on GPS instead of remembering routes—or how driving an automatic car changes focus. Could medicine be facing a similar shift?

2️⃣ “Will My Expertise Be Devalued by Machines?” (Bangladesh, 2024)
Healthcare professionals shared concerns about:

• Job security and evolving roles 💼
• Ethics, accountability, and trust ⚖️
• Losing the human touch ❤️
• The need for AI training and oversight 📚

AI adoption isn’t just technical—it’s behavioral, cultural, and deeply human.

My Take:

I see AI as a partner, not a threat. I use it every day for research and content, but I never outsource judgment. AI can boost efficiency—but only if we stay curious, critical, and engaged.

We can’t let convenience replace competence. AI should augment our expertise, not erode it.

🌍 PathVision 2025 — Sept 5–7, 2025

I’m also thrilled to share that I’ll be livestreaming PathVision 2025 from September 5–7, 2025, on LinkedIn and YouTube! 🎥

This year’s conference is packed with innovations in AI, digital pathology, and cancer diagnostics. I’ll bring you live insights, interviews, and key takeaways from the sessions—so mark your calendars and tune in!

🧩 Key Takeaways

• Continuous AI use may lower independent performance.
• Professionals worry about trust, ethics, and losing skill.
• The goal isn’t to resist AI—but to use it critically and consciously.
• The best outcomes happen when AI and human expertise work together.

🕒 Episode Highlights

• 00:00–06:14 | Welcome & PathVision preview
• 06:14–17:46 | AI deskilling question
• 08:27–14:05 | Colonoscopy study results
• 27:11–38:10 | Healthcare workers’ AI concerns
• 43:59–51:05 | Reflections & responsible AI use
• 51:05–52:55 | Closing thoughts + PathVision invite

🧭 Mentioned

• Lancet Gastroenterology & Hepatology (2025): “Endoscopist deskilling risk after AI exposure”
• Bangladesh Study (2024): “Will my training be devalued by machines?”
• My Book: Digital Pathology 101 (Updated Edition Coming Soon)
• Event: PathVision 2025 – Sept 5–7, 2025 (Streaming Live!)

Thanks for listening to DigiPath Digest #29! I hope it inspires you to think critically about how we can embrace AI without losing what makes us human.

And don’t miss PathVision 2025 (Sept 5–7, 2025)—I’ll be streaming it live for three days of insights, innovation, and community. Let’s keep learning and leading the future of digital pathology tog

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What if climbing the digital pathology “mountain” isn’t about reaching the summit alone—but knowing where base camp is, and who you bring with you?

In this episode, I take you inside the Digital Diagnostic Summit in Park City, hosted by Lumea, where fewer than 100 digital pathology leaders gathered to share their journeys, challenges, and solutions.

From resilient metaphors of Everest climbs to practical strategies for workflow ownership, clinical trials, and AI-powered biomarkers, this summit showed that the future of diagnostics is built on collaboration, purpose-driven adoption, and trust in data custodianship.

🔑 Highlights with Timestamps

• [00:03–01:49] Summit kickoff – “Climbing the Digital Pathology Mountain” theme and why this summit feels different.
• [01:49–03:38] Everest keynote – lessons in resilience and why failure is part of innovation.
• [03:38–06:02] Collaboration over competition – why base camp is as important as the summit.
• [06:02–09:18] Workflow ownership – defining value-driven outcomes before choosing tools.
• [09:18–11:35] Data custodianship – protecting patient privacy while enabling ethical research.
• [11:35–15:16] Panel insights – choosing digital tools that integrate into workflows and prevent burnout.
• [15:16–17:31] Horseback networking – why informal conversations matter as much as panels.
• [17:31–19:18] Emerging health tech – 3D printing prosthetics and synthetic blood innovations.
• [19:18–23:54] Personalized biomarkers – outcome-driven diagnostics that move beyond human scoring.
• [23:54–29:16] Digital pathology in trials – Aperture platform launch and patient stratification in global studies.
• [29:16–31:42] Community impact – stories of career transformation and remote adoption.
• [31:42–32:37] Closing thoughts – why intimate summits accelerate adoption and what’s next.

📚 Resources from this Episode

• FDA Journal of Pathology & Informatics – Research on data custodianship and ethical use.
• Proscia Aperture Platform – New tool for clinical trial management and patient identification.
• Astro Zenica Digital Biomarker – Personalized biomarker validated by outcomes.
• Barco Healthcare White Paper – Why display quality matters in pathology.

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✨ Key Insights from the Summit

✔ Success in digital pathology is not about scaling alone—partnerships matter.
✔ Labs must own their workflows and define outcomes before adopting tools.
✔ Data custodianship is central for protecting privacy while advancing research.
✔ Personalized biomarkers are shifting diagnostics toward outcome-driven AI.
✔ Clinical trials benefit from digital pathology in patient selection and stratification.
✔ Intimate summits provide mentorship, collaboration, and career transformation.
✔ Exciting health tech—from synthetic blood to 3D printing—complements digital pathology innovation.

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What if up to 35% of the diagnostic color data on your pathology slides never reaches your eyes—just because of your monitor? In this episode, sponsored by Barco, I sit down with Dr. Monika Lamba Saini (ADC Therapeutics) and Tom Kimpe (Barco) to uncover why color calibration in digital pathology isn’t optional anymore—it’s critical for diagnosis, efficiency, and AI readiness.

Highlights:

• [00:03:42] Monika’s path from CROs to biopharma and why color consistency matters in clinical trials.
• [00:09:22] What “color science” means in pathology and why color is one-third of diagnosis.
• [00:12:40] When the same tissue looks different across labs and scanners—and how this causes diagnostic conflicts.
• [00:16:19] Why HER2 scoring and IHC rely on color intensity—and how poor color fidelity lowers diagnostic confidence.
• [00:18:34] Research showing up to 35% of H&E slide colors fall outside of the sRGB color space—meaning you never see them on a standard monitor.
• [00:22:23] Where the biggest sources of color variability occur across the imaging chain come from.
• [00:26:26] Calibrated displays and pathologist speed—why confidence = faster reads.
• [00:35:19] How monitors degrade over time and why calibration is essential.
• [00:41:27] Why choosing a monitor based on price is short-sighted—and the real ROI of medical-grade displays.
• [00:43:45] ICC profiles explained: the missing piece in end-to-end color consistency.
• [00:52:48] Training pathologists on color literacy and internal calibration strategies.
• [01:00:10] How color variability affects AI algorithm accuracy—up to a 30% drop if scanners differ.
• [01:14:57] The role of professional societies in building color literacy and regulatory guidance.
• [01:22:30] Final takeaways: if you’re skeptical about calibration, here’s why you should care.

Resources from this Episode

• FDA Research by Cheng – H&E slide colors beyond sRGB Reproducible Color Gamut of Hematoxylin and Eosin Stained Images in Standard Color Space.
• Barco White Paper – The Importance of Color in Modern Pathology.
• Barco eBook – Digital Pathology: What Are The Benefits
• Barco MDPC-8127 Monitor – Medical-grade display optimized for pathology.

Digital Pathology 101 (by me, Dr. Aleksandra Zuraw) – Free PDF & Amazon print edition.

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7 Counterintuitive Secrets from NCCN’s 2025 AI in Cancer Care Summit

When the National Comprehensive Cancer Network (NCCN) gathers healthcare leaders, people listen. I attended the 2025 Policy Summit on the evolving AI landscape in cancer care—and walked away with insights that were raw, practical, and surprisingly hopeful.

Instead of hype or overpromising, cancer care leaders shared honest strategies for implementing AI responsibly and effectively. In this episode, I break down the 7 counterintuitive secrets they’re using to fast-track adoption—while others remain stuck.

Whether you’re in digital pathology, oncology, or healthcare AI, these lessons matter for your projects.

KEY HIGHLIGHTS

• 0:04 – Reporting from Washington DC: what the NCCN AI Policy Summit revealed about the real state of AI in cancer care.
• 1:10 – Why NCCN guidelines shape cancer care worldwide.
• 1:36 – Even top cancer centers struggle with AI implementation—why delays and budget overruns are common.
• 3:16 – Secret #1: Stop chasing perfect AI tools—build strategic guardrail frameworks instead.
• 6:20 – Secret #2: Plan for biological drift from day one.
• 9:29 – Secret #3: Target underutilized care areas, not your strongest programs.
• 12:07 – Secret #4: Design AI for patients receiving care, not just providers giving it.
• 16:29 – Secret #5: Follow the pioneers—don’t reinvent from scratch.
• 19:09 – Secret #6: Build flexible systems for evolving regulatory pathways.
• 22:09 – Secret #7: Stop using human-level performance as the gold standard.
• 31:23 – Why integration is now as important as innovation in AI for pathology.
• 34:31 – What’s next: NCCN will publish a report based on these discussions.

THIS EPISODE'S RESOURCES

• NCCN – National Comprehensive Cancer Network
• Episode with Dr. Lija Joseph on patient-pathologist communication
• Aeffner F. et al. – The Gold Standard Paradox in Digital Image Analysis: Manual vs Automated Scoring as Ground Truth
• Artera AI FDA de novo authorization news (August 2025)
• Maryland AI Regulation (effective October 1, 2025)

If this episode resonated with you, please share it with colleagues. Speaking the same language around digital pathology and AI implementation will help us all move forward.

🎧 Thank you for trailblazing with me. Until next time, keep trailblazing however you can.

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What if the way we quantify pathology is more guesswork than science? In this episode of DigiPath Digest, I take you through the latest research where AI is not just supporting but challenging traditional methods of image analysis in neuropathology, nephrology, hematology, and cytology. From Boston brain banks to Mayo Clinic kidney models, we look at how advanced AI compares to human vision—and where it already outperforms us.

Episode Highlights:

• [00:02:49] Neuropathology image analysis (Boston VA & BU) – Why traditional semiquantitative scoring often fails, and how AI-based density quantification reveals more subtle pathology in CTE.
• [00:13:16] Chronic kidney changes with AI (Mayo Clinic, Cambridge, Emory, Geneva) – A 20-class AI model trained on 20,500 annotations, showing how multiclass segmentation outperforms human guesswork in renal pathology.
• [00:21:09] Digital hematology review (University of Pennsylvania) – Current hurdles in AI for blood and bone marrow evaluation: regulatory oversight, data standardization, and resistance to change.
• [00:25:52] AI in cytology review (Journal of Cytopathology) – From BD FocalPoint to deep learning: two decades of digital cytology, stagnation, and why adoption still lags despite proven benefits.
• [00:32:09] Neuropathology goes digital – Where digital neuropathology is already routine (Ohio State, Mayo Clinic, Leeds, Granada) and why this specialty is crucial for pushing adoption.
• [00:34:19] Personal note – Why I believe learning, sharing, and experimenting with AI tools now will shape the way we practice pathology tomorrow.

Resources from this Episode

• Comparison of quantitative strategies in neuropathologic image analysis – Boston VA / BU Brain Bank study.
• Multiclass AI model for chronic kidney changes – Mayo Clinic, Cambridge, Emory, Georgia Tech, Geneva collaboration.
• Review: Digital hematology in the AI era – International Journal of Laboratory Hematology.
• Review: AI and machine learning in cytology – Journal of the American Society of Cytopathology.
• Digital Pathology 101 (by me, Dr. Aleksandra Zuraw) – Free PDF & Amazon print edition.
• Pathology AI Makeover Course – Practical training for AI in pathology workflows.

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What if the AI tools we trust for cancer diagnosis are not always correct? This episode of DigiPath Digest takes on the uncomfortable but critical question: can AI “lie” to us—and how do we verify its performance before adopting it in clinical practice?

Highlights:

• [00:02:00] Foundation models in action: Deployment of a fine-tuned pathology foundation model for EGFR biomarker detection in lung cancer—reducing the need for rapid molecular tests by 43%.
• [00:08:41] Bone marrow AI misclassifications: Why automated digital morphology still struggles with consistency across leukemia and lymphoma cases.
• [00:14:45] Lossy DICOM conversion: How file format changes can subtly—but significantly—affect AI model performance.
• [00:21:45] Federated tumor segmentation challenge: Coordinating 32 international institutions to benchmark healthcare AI fairly across diverse datasets.
• [00:27:47] AI in gynecologic cytology: Reviewing AI-driven Pap smear screening—promise, limitations, and why rigorous validation remains essential.
• [00:32:27] Takeaway: Trust but verify—AI tools must be validated before they can support or replace clinical decisions.

Resources from this Episode

• Nature Medicine – Fine-tuned pathology foundation model for lung cancer EGFR biomarker detection.
• Scientific Reports (Germany) – Study on how DICOM conversion impacts AI performance in digital pathology.
• Federated Tumor Segmentation Challenge – Benchmarking AI across 32 global institutions.
• Acta Cytologica – Review on AI in gynecologic cytology and Pap smear screening.

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What if AI could predict cancer outcomes better than traditional methods—and at a fraction of the cost? In this episode, I explore how multimodal AI is reshaping lung and prostate cancer predictions and why integration challenges still stand in the way.

Episode Highlights with Timestamps:

• [00:02:57] Agentic AI in toxicologic pathology – what it is and how it could orchestrate workflows.
• [00:05:40] Grandium desktop scanners – making histology studies more accessible and efficient.
• [00:08:03] Clover framework – a cost-effective multimodal model combining vision + language for pathology.
• [00:13:40] NSCLC study (Beijing Chest Hospital) – AI predicts progression-free and overall survival with high accuracy.
• [00:17:58] Prostate cancer prognostic model (Cleveland Clinic & US partners) – validating AI-enabled Pathomic PRA test.
• [00:23:35] Thyroid neoplasm classification – challenges for AI in distinguishing overlapping histopathological features.
• [00:34:49] Real-world Belgium case study – AI integration into prostate biopsy workflow reduced IHC testing and turnaround time.
• [00:41:03] Lessons learned – adoption hurdles, system integration, and why change management is essential for successful digital transformation.

Resources from this Episode

• World Tumor Registry – A global open-access repository for histopathology images: World Tumor Registry
• Beijing Chest Hospital NSCLC AI Prognostic Study – Prognosis prediction using multimodal models.
• Cleveland Clinic Pathomic PRA Study – Independent validation of AI-enabled prostate cancer risk assessment.
• Grandium Scanners – Compact desktop scanners for histology slides: Grandium.ai

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“AI in Pathology Isn’t Coming — It’s Already Here. Are You Ready?”

From confusion to clarity — that’s what this episode is all about. I sat down with Drs. Liron Pantanowitz, Hooman Rashidi, and Matthew Hanna to dissect one of the most important and comprehensive AI-in-pathology resources ever created: the 7-part Modern Pathology series from UPMC’s Computational Pathology & AI Center of Excellence (CPAiCE). This isn’t just another opinion piece — it's your complete guide to understanding, implementing, and navigating AI in pathology with real-world insights and a global lens.

Together, we discuss:

• Why pathologists and computer scientists are often lost in translation
• How AI bias, regulation, and ethics are being addressed — globally
• What it really takes to operationalize AI in patient care today

If you’ve ever asked, “Where do I even start with AI in pathology?” — this is your answer.

🔍 Highlights & Timestamps
00:00 – The importance of earned trust in AI
01:00 – Education gaps in AI for both pathologists & developers
03:00 – Why CPAiCE was built & the three missions it serves
07:00 – The seven-part series: a blueprint for AI literacy
10:00 – Making AI education accessible without losing technical integrity
13:00 – How this series is being used for global teaching (including by me!)
17:00 – Generative AI in creating figures vs. human-authored content
21:00 – Eye-opening global AI regulations that pathologists MUST know
24:00 – Ethics, bias & strategies to mitigate real clinical risks
30:00 – What’s next: CPAiCE’s mission to reshape pathology education & practice
34:00 – A teaser: the first CPAiCE textbook is on the way!

📚 Resources from This Episode

📰 Read the full series (open access!):
Modern Pathology 7-Part AI Series: https://www.modernpathology.org/article/S0893-3952(25)00001-8/fulltext

👨‍⚕️ UPMC’s Computational Pathology & AI Center of Excellence (CPAiCE)
🌍 Creative Commons licensing means YOU can reuse, remix & teach from these resources — just cite the source.

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Can AI Grade Cancer Better Than Us? The Truth About T-Cell Imaging, Biomarkers & Digital Pathology DisruptionYou think Saturday mornings are for coffee? Try diving into bone marrow morphology, organ donor kidney biopsies, and AI-driven metastasis detection at sunrise. That’s how I do it—and you’re invited to join.

Welcome to another data-packed episode of DigiPath Digest, where we explore the latest frontier in digital pathology and AI. This time, I reviewed some of the most exciting recent abstracts spanning cancer grading, T-cell quantification, and AI agents in oncology decision-making.

These studies aren’t just fascinating—they’re redefining what’s possible in diagnostics, especially in under-resourced areas where digital pathology can create game-changing access and efficiency.

🔬 Highlights with Timestamps

[00:04:00] Detecting Metastases with Vision Transformers
A team from Leeds Teaching Hospital developed a model for identifying lymph node and omental metastases in ovarian and peritoneal cancers with 99.8% AUROC and 100% balanced accuracy—this isn’t hype; it’s real AI pre-screening that could reduce diagnostic strain on pathologists.

[00:08:00] DeepHeme: Bone Marrow Smears Meet AI
UCSF and Memorial Sloan Kettering collaborated on DeepHeme, an ensemble deep learning model that classifies bone marrow aspirate cells with expert-level accuracy. With over 30K training images and strong external validation, it outperforms humans in both speed and detail.

[00:16:00] Multimodal AI for Head & Neck Cancer
This review showcases how integrating radiology, histopathology, and genomics with AI enhances personalized treatment and prognosis. Spoiler alert: Multimodal > unimodal.

[00:24:00] Real-Time Kidney Biopsy Evaluation via AI
Shoutout to our Digital Pathology Place sponsor, Techcyte, for their AI-powered tool improving accuracy and halving the time it takes to evaluate frozen kidney biopsies. This is the kind of innovation we need in organ transplantation.

[00:32:00] GPT-4 as an Oncology Agent?
Heidelberg researchers created an autonomous AI agent using GPT-4 plus vision models and OncoKB to handle oncology case decisions with 91% accuracy. This isn’t ChatGPT guessing—it’s a hybrid system citing guidelines and performing complex reasoning.

🧠 Resources From This Episode

• 📰 Multiple Instance Learning for Metastases Detection in Ovarian Cancer – Cancers journal
• 🧬 DeepHeme: Generalizable Bone Marrow Cell Classifier – Science Translational Medicine
• 📚 AI in Head and Neck Cancer: A Multimodal Review – Cancers journal
• 🧪 AI-Assisted Review of Donor Kidney Pathology – Techcyte & Digital Pathology Place demo
• 🤖 Autonomous AI Agent for Oncology Decisions – Heidelberg Group
• 🎙️ Podcast on GPT-4 agents with Dr. Nina Kolker
• 🧵 Earrings mentioned in the livestream? Find them in the DPP Store

I’d love to hear your feedback, your projects, and what digital pathology means to you. You can always reach out through comments, LinkedIn, or email.

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AI Pathology & Genomics: A New Benchmark for Predicting Gene Mutations

If you still think visual quantification is “good enough” in pathology, think again.
In this 27th episode of DigiPath Digest, I break down four transformative abstracts that show how AI is shifting our diagnostic landscape—from breast cancer segmentation to fibrosis assessment, and all the way to spatial immunology and the evolving immunoscore.

If you’re still relying on manual scoring, static staging systems, or single-marker immunohistochemistry, this episode will challenge you to look deeper—literally and algorithmically.

🔬 Episode Highlights & Timestamps

[02:00] Abstract 1 – AI + IHC for epithelial cell segmentation in breast cancer
[07:30] Abstract 2 – Deep learning quantifies TILs in esophageal cancer
[14:30] Abstract 3 – Biopsy size impacts SHGTPF-based liver fibrosis staging
[22:30] Abstract 4 – Immunoscore in colorectal cancer: promise & limits

🧬 Key Insights & Takeaways

1. IHC-Guided Segmentation for Breast Cancer
Using immunohistochemistry as a ground truth for AI segmentation reveals how effective our models can be—but also where they fall short. The challenge? Accurately subclassifying benign, in situ, and invasive epithelial cells. Spoiler: We’re not quite there yet.

2. Tumor-Infiltrating Lymphocytes in Esophageal SCC
A Chinese team trained deep learning algorithms to analyze TILs spatially. Result? High TIL counts in both intra- and peritumoral zones correlated with better survival—highlighting the emerging power of spatial immunology.

3. Liver Fibrosis Staging with SHGTPF Microscopy
Second harmonic generation two-photon microscopy gives us label-free imaging of unstained tissue. The takeaway: bigger biopsies (20–26mm) yield better fibrosis quantification. Biopsy position? Surprisingly irrelevant. A game-changer for MASLD diagnostics.

4. Immunoscore for Colorectal Cancer
This image analysis-based tool outperforms traditional TNM staging, helping stratify patients for immunotherapy. But adoption is hampered by cost and digital slide access. Integrating AI could take it to the next level—something we should all watch closely.

🎓 Resources from This Episode

• Breast cancer segmentation using IHC-guided AI (Trondheim, Norway)
• Esophageal SCC & spatial TILs (Cancer Medicine, China)
• SHGTPF microscopy in liver fibrosis (UK/US multi-center study)
• Immunoscore in colorectal cancer (Jerome Galon group origins)

💡 Bonus: I show off some histology-inspired earrings and talk about the story behind them—multinucleated giant cells, cartilage, and more. Check them out if you’re into pathology fashion!

We’re not just validating AI anymore—we're redefining diagnostics. From high-res, label-free imaging to robust spatial biology insights, the path forward in pathology is clearer and more precise than ever. Whether you’re a practicing pathologist, researcher, or innovator, this episode offers tools and perspectives you can apply today.

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If our visual scoring is still based on gut feeling, how do we scale precision?
In this week’s DigiPath Digest, I explored four new AI-focused papers that could reshape how we diagnose prostate, bladder, gastroesophageal, and endocrine cancers.

From automated IHC scoring to predicting urethral recurrence post-cystectomy, these studies highlight the growing value—and responsibility—of integrating AI into our pathology workflows.

And yes, I also reveal where to get my histology-inspired earrings 😉

Episode Highlights

[06:00] Muse Vet Platform launch + STP talk
[11:00] Tools I use: Perplexity, RAG, ChatGPT, and AI citation traps
[14:00] AI’s promise—and its pitfalls

Paper 1: IHC Scoring in GEC (Caputo et al.)

Manual PD-L1 and HER2 scoring is subjective. This study shows AI can standardize and improve accuracy using digital tools for GEC.

[20:00] AI reduces visual bias
[23:00] Potential to replace expensive assays

Paper 2: ASAP in Prostate Biopsies

Page Prostate AI matched final diagnoses 85% of the time—more than human reviewers.

[24:00] ASAP = gray zone diagnosis
[27:00] AI matched final calls more often than humans

Paper 3: Recurrence Prediction Post-Cystectomy

Chinese study developed a recurrence model using ML on clinical data. AUC: 0.86 (train), 0.77 (test).

[30:00] Risk factors: CIS, bladder neck involvement
[32:00] SHAP explained model insights

Paper 4: Reticulin Framework in Endocrine Pathology

Reticulin stains are cheap but powerful. This paper calls for AI to take notice.

[36:00] Reticulin separates benign from malignant
[40:00] Let’s train AI on these patterns

📚 Resource from this Episode

• Caputo et al., Pathology Research & Practice
• Page Prostate study on ASAP
• ML model predicting urethral recurrence
• Reticulin stains in endocrine tumor grading

AI is already enhancing diagnostic precision—we just need to guide its use responsibly. From special stains to advanced models, this episode covers where we're headed next.

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If we don’t learn to work with LLMs now, we might end up competing with them. 🧠
In this week’s DigiPath Digest, I return to our Journal Club to unpack the latest research on AI in tumor classification, focusing on GPT-4o, LLaMA, and other LLMs. Can these models really outperform traditional tools when analyzing pathology reports?

Surprisingly—yes. But don’t panic. This episode is about understanding what LLMs actually bring to the table, how they’re being evaluated, and what we need to consider as digital pathology continues to evolve.

It’s also a special week for me personally—I recorded this episode the morning of my U.S. citizenship ceremony, and I used AI to help write my speech! I’ll share more about that next time.

⏱️ Episode Highlights

[00:00] – Life update + AI-written speech for my citizenship
[04:00] – Journal Club: Austrian study on LLMs in pathology report analysis
[05:00] – Why cancer registries need better documentation tools
[06:00] – LLMs tested on synthetic pathology reports—game-changing idea
[07:00] – GPT-4 and LLaMA outperform score-based models in accuracy
[08:00] – Use case: AI-enhanced text mining across whole archives
[09:00] – How my PhD could’ve been easier with these tools
[10:00] – Second paper: A public synthetic dataset for benchmarking LLMs
[11:00] – Tools used: ChatGPT, Perplexity, Copilot to generate report variations
[13:00] – Benefits of synthetic data for de-identification
[14:00] – Thoughts on bias, annotation workflows, and future-proofing
[16:00] – Polish research on hybrid annotation for follicular lymphoma
[19:00] – Foundation models, bootstrapping, weak supervision in action
[22:00] – Charles River: AI for thyroid hypertrophy scoring in tox path
[23:00] – Subjectivity of scoring thresholds and reproducibility
[24:00] – Morphology-driven scoring architecture improves accuracy

📚 Resource from this Episode

1. LLM Performance in Malignancy Detection from Pathology Reports
   🔗 Read Article
2. Synthetic Dataset for Evaluating LLMs in Medical Text Classification
   🔗 Read Article

🧰 Tools & Topics Mentioned

• LLMs: GPT-4o, LLaMA, Copilot, Perplexity
• Synthetic Data for AI model testing
• Annotation strategies: weak supervision, bootstrapping
• Pathology AI applications: tumor detection, thyroid activity, lymphoma
• Research teams: Austria, Poland, Charles River Labs

The big takeaway? AI tools are improving fast—and it’s up to us to decide how they’re used in our field. This episode breaks down the latest advancements and opens the door to practical, safe integration in pathology workflows.

🎧 Let’s keep pushing the boundaries—together.

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AI in Pathology: ML-Ops and the Future of Diagnostics

What if the most advanced AI models we’re building today are doomed to die in the machine learning graveyard? 🤯 That’s the haunting question I tackled in the final episode of our 7-part series exploring the Modern Pathology AI publications.

In this session, I explored machine learning operations (ML-Ops)—what they mean for digital pathology —and why even the most brilliant algorithm can fail without proper deployment strategies, data infrastructure, and lifecycle management.

But we don’t stop there. I take you on a future-forward tour through multi-agent frameworks, edge computing, AI deployment strategies, and even virtual/augmented reality for medical education. This isn’t sci-fi. This is happening now, and as pathology professionals, we need to be prepared.

🔗 Full episode reference:
Modern Pathology - Article 7: AI in Pathology ML-Ops and the Future of Diagnostics
Read the paper

🔍 Episode Highlights & Timestamps

[00:00] – Tech check, community shout-outs, and livestream reflections
[02:00] – Overview of ML-Ops: What it is and why pathologists should care
[03:45] – What’s a Machine Learning Graveyard? Personal examples of models I’ve built that went nowhere
[05:30] – Machine learning platforms: from QPath to commercial image analysis tools
[06:45] – The lifecycle of ML models: Development, deployment, and monitoring
[09:00] – Mayo Clinic and Techcyte partnership: Real-world deployment integration
[12:30] – Frameworks & DevOps tools: Docker, Git, version control, metadata mapping
[14:30] – Model cards in pathology: Structuring ML model metadata
[16:30] – Deployment strategies: On-premise, cloud, and edge computing
[20:00] – PromanA and QA via edge computing: Doing quality assurance during scanning
[23:00] – Measuring ROI: From patient outcomes to institutional investment
[25:00] – Multi-agent frameworks: AI agents collaborating in real-time
[28:00] – Narrow AI vs. General AI and orchestrating narrow tools
[30:00] – Real-world applications: Diagnosis generation via AI collaboration
[32:00] – Virtual & Augmented Reality in pathology training: From smearing to surgical simulation
[35:00] – AI in drug discovery and virtual patient interviews
[38:00] – Scholarly research with LLMs: Structuring research ideas from unstructured data
[41:00] – Regulatory considerations: Recap of episode 5 for frameworks and guidelines
[42:00] – Recap and future updates: Book announcements, giveaways, and next steps

Resource from this episode

• 🔗 Modern Pathology Article #7: AI in Pathology ML-Ops and the Future of Diagnostics
• 🛠️ Tools/References mentioned:
  • QPath (Free Image Analysis Tool)
  • Techcyte & Aiforia for model development and deployment
  • PromanA for edge computing and real-time QA
  • Model Cards (Pathology-specific metadata structure)
  • Apple Vision Pro, Meta Oculus, HoloLens for VR/AR learning
  • Dr. Hamid Ouiti Podcast on software failure in medicine
  • Dr. Candice Chu's AI-assisted academic writing frame

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Can We Ever Eliminate Bias in AI for Pathology?

Every time we think we’ve trained a “neutral” algorithm, we discover our own fingerprints all over it. Our biases. Unconscious. Systemic. Data-driven. And if we ignore them, AI won’t just fail—it will fail patients.

Welcome back, my digital pathology trailblazers! In this sixth episode of our 7-part AI in Pathology series, we tackle one of the most uncomfortable yet necessary conversations: Ethics and Bias in AI and Machine Learning. These are not abstract philosophical concerns—they are critical decisions that affect diagnostic accuracy, fairness, and patient safety.

We lean heavily on the brilliant work co-authored by Matthew Hanna, Liam Pantanowitz, and Hooman Rashidi, published in Modern Pathology, which you can read here: Ethics and Bias in AI for Pathology.

Let’s explore where bias creeps in, how we can mitigate it, and what it means to be a responsible data steward in digital pathology.

⏱️ Highlights & Timestamps

[00:00:00] Welcome back! Kicking off from Pennsylvania at 6:00 AM and reflecting on USCAP highlights, upcoming podcasts, and a pivotal lawsuit on LDTs.
[00:03:00] Defining today’s topic: Bias in AI—why it matters, and how pathologists are key players in shaping ethical, trustworthy algorithms.
[00:05:00] Who are the “data stewards”? A new term you need to own. We explore the role of healthcare professionals in AI development and deployment.
[00:07:00] Ethical principles decoded—autonomy, beneficence, non-maleficence, justice, and accountability—and how they translate to AI and ML.
[00:11:00] From voting rights to data rights: A surprising analogy from my U.S. citizenship interview about the evolution of fairness.
[00:12:00] 12 types of bias explained—from data bias to feedback loops, representation to confirmation bias—with real pathology examples.
[00:22:00] Temporal bias and transfer bias: Why yesterday’s data may not apply to today’s patients.
[00:26:00] Walkthrough of the AI lifecycle and how bias seeps in at every stage—from research to regulatory approval.
[00:29:00] Clinical trials & guidelines: Learn the difference between STARD-AI, TRIPOD-AI, QUADAS-AI, and CONSORT-AI.
[00:33:00] Visual case study: Gleason score distribution by region shows how biased training data leads to misdiagnosis.
[00:37:00] Real-world mitigation: I spotlight Digital Diagnostics Foundation and Big Picture Consortium as proactive models for bias reduction.
[00:41:00] Why explainability and introspection are more than buzzwords—they are our tools for ensuring accountability.
[00:44:00] FAIR data principles—Findability, Accessibility, Interoperability, and Reusability—and why annotations often fall short.
[00:48:00] Practical steps: How to build better algorithms with built-in fairness, bias detectors, and responsible data sharing.

📚 Resource from this Episode:

📄 Featured Publication:
Ethics and Bias Considerations in Artificial Intelligence and Pathology
➡️ Access Full Article

Let’s keep creating technology that doesn’t just do what we tell it to—but does what is right for everyone. See you in the next and final episode of this transformative AI series.

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The Most Overlooked Risk in AI for Pathology? It’s Not What You Think…

Welcome, my trailblazing digital pathologists! In this episode, I dive headfirst into the regulatory maze of Artificial Intelligence (AI) in pathology, covering global frameworks, safety risks, ethics, and the future of software as a medical device. While regulation might not be the flashiest part of AI, ignoring it could cost us innovation—or worse, patient safety.

We’re on Part 5 of our 7-part AI in Pathology series, and this one’s vital for anyone developing, using, or simply curious about AI and machine learning tools in healthcare.

If you thought regulation was boring, think again—it’s what separates a helpful algorithm from a dangerous black box.

🎧 Listen to the full episode and reference the latest study discussed here: Modern Pathology Journal Article

🔍 Highlights & Timestamps

[00:00:00] Welcome & Why Regulation Matters
Pixelation aside, I introduce today's critical topic—regulatory frameworks that define how AI tools are used, approved, and reimbursed in pathology.

[00:03:00] The Risks AI Brings to Healthcare
We’re not just talking about patient data—think security, ethical biases, economic consequences, and even environmental impact from heavy computation.

[00:05:00] HIPAA, GDPR, and the Common Rule Explained
What protects patient privacy globally? Dive into U.S. and European legislation like HIPAA and GDPR, and how IRBs and the Common Rule ensure ethical compliance in clinical research.

[00:08:00] FDA & Global Agencies Breakdown
Get to know the role of Health Canada, UK’s MHRA, Japan’s PMDA, and others in approving AI tools. Discover how the U.S. FDA sets the gold standard—and why CE Mark devices often hit Europe first.

[00:17:00] What Makes Software a Medical Device (SaMD)?
Four critical questions to ask to determine if your AI tool is considered a regulated device. If the software diagnoses, directs, or lacks transparency—chances are, it’s a device.

[00:22:00] FDA Pathways: Clearance vs. Approval
I break down Class I, II, and III device categories, and what 510(k) clearance means versus pre-market approval (PMA). Yes, we even cover why some tools like Paige AI needed full PMA.

[00:40:00] Why Reimbursement Is the Elephant in the Room
No billing codes, no incentives. I share a personal story about my husband’s five-year reimbursement battle and the challenges of proving economic and clinical value.

[00:45:00] The LDT (Lab Developed Test) Controversy
In 2024, the FDA formally categorized LDTs as medical devices, igniting debate about oversight, innovation, and compliance.

[00:47:00] Generative AI: A New Beast to Regulate
ChatGPT and similar tools pose fresh challenges: reproducibility, explainability, and dynamic outputs. Current frameworks simply can’t keep up—but regulation must.

[00:51:00] USCAP Event Invite & Digital Pathology Collaboration
I’m thrilled to invite you to Muse Microscopy’s USCAP presentation—join live or virtually! Plus, learn how we’re reshaping the digital pathology workflow with direct-to-digital imaging.

🧠 Resource From This Episode

Referenced Study:
📄 Artificial Intelligence in Pathology: Regulatory Challenges & Opportunities
👉 Read the

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In this episode sponsored by Epredia, Dr. Anil Parwani explores the transformative journey of digital pathology from basic slide scanning to AI-driven diagnostics. He shares real-world implementation experiences and demonstrates how these technologies are addressing critical challenges in pathology practice.

• Pathology faces increasing demands amid workforce shortages and knowledge explosion
• Digital pathology provides standardization, objectivity, and automation beyond glass slides
• Ohio State University has scanned 4.2 million slides representing nearly 500,000 cases since 2016
• Current AI applications include biomarker quantification, rare event detection, and tumor classification
• Integration challenges remain the primary barrier to seamless adoption of AI tools
• Future technologies include virtual staining, 3D pathology, and large language model integration
• Artificial intelligence remains task-oriented while real intelligence is context-aware and knowledge-based
• Each institution must navigate their own "digital pathology chasm" based on specific needs
• Digital tools will augment pathologists' capabilities rather than replace human expertise
• The technology marketplace offers solutions for every stage of the digital transformation journey

This Episode's Resources

• Epredia Digital Pathology Website

This Episode on YouTube

Coming soon!

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You might be using AI models in pathology without even knowing if they’re giving you reliable results.

Let that sink in for a second—because today, we’re fixing that.

In this episode, I walk you through the real statistics that power—and sometimes fail—AI in digital pathology. It's episode 4 of our AI series, and we’re demystifying the metrics behind both generative and non-generative AI. Why does this matter? Because accuracy isn't enough. And not every model metric tells you the whole story.

If you’ve ever been impressed by a model’s "99% accuracy," you need to hear why that might actually be a red flag. I share personal stories (yes, including my early days in Germany when I didn’t even know what a "training set" was), and we break down confusing metrics like perplexity, SSIM, FID, and BLEU scores—so you can truly understand what your models are doing and how to evaluate them correctly.

Together, we’ll uncover how model evaluation works for:

• Predictive Analytics (non-generative AI)
• Generative AI (text/image generating models)
• Regression vs. Classification use cases
• Why confusion matrix metrics like sensitivity and specificity still matter—and when they don’t.

Whether you're a pathologist, a scientist, or someone leading a digital transformation team—you need this knowledge to avoid misleading data, flawed models, and missed opportunities.

🕒 EPISODE HIGHLIGHTS WITH TIMESTAMPS

• [00:00] Warm greetings and a peek into my citizenship journey 👋
• [02:30] How exam attire differs across countries
• [04:00] Model evaluation isn't about memorizing metrics—it's about understanding concepts
• [06:30] Story: My first exposure to AI misuse in pathology
• [08:00] Confusion matrix basics: TP, FP, TN, FN
• [11:00] Metrics breakdown: Accuracy, Sensitivity, Specificity, F1-score
• [15:00] Regression-based metrics and why they matter
• [18:00] Statistical challenges in Generative AI
• [21:00] What is "Perplexity" and why low scores matter
• [24:00] BLEU, ROUGE, and Next Sentence Prediction explained
• [28:00] SSIM and FID scores for image quality in AI
• [31:00] When metrics mislead: superficial similarity vs. real insight
• [35:00] Best practices: Ensemble models, human-in-the-loop, and adversarial testing
• [43:00] Choosing the right metric for the right model
• [46:00] Closing thoughts on trust, testing, and trailblazing

📘 RESOURCE FROM THIS EPISODE:

🔗 Read the full paper discussed in this episode:
"Statistics of generative and non-generative artificial intelligence models in medicine"

💬 Final Thoughts

Statistical literacy isn’t optional anymore—especially in digital pathology. AI isn’t just a buzzword; it’s a tool, and if we want to lead this field forward, we must understand the systems we rely on. This episode will help you become not just a user, but a better steward of AI.

🎙️ Tune in now and let's keep trailblazing—together.

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What if I told you the biggest AI breakthroughs in pathology aren’t coming from ChatGPT or generative tools—but from the quiet power of predictive analytics and machine learning?

In this episode, I explore the non-generative side of artificial intelligence in pathology. These are the tools that detect tumors, segment tissue, classify images, and make predictions—without generating a single word.

It’s the third chapter in our guided AI series, and this time we focus on the models you’re more likely to use in real-world diagnostics. You’ll hear about object detection, segmentation, anomaly detection, and how these models are built using supervised and unsupervised learning—plus the pros and cons of different annotation strategies.

We’ll also cover why no one model fits all, and how combining simple tools like decision trees with more complex neural networks is often the key to building reliable, usable AI in pathology.

Whether you’re training your first model, selecting an algorithm for rare disease detection, or just want to understand what “unsupervised clustering” means—you’ll find something useful here.

🎯 HIGHLIGHTS WITH TIMESTAMPS

• [00:00] Welcome and global audience shout-outs
• [02:00] News: The authors of the AI paper series are coming on the show!
• [04:00] Booth 528 @ USCAP—join me live
• [06:00] Live annotation workshop announcement
• [08:00] AI Hierarchy: ML → Deep Learning → Foundation Models
• [10:00] Use Cases: Object detection, segmentation, anomaly detection
• [14:00] Supervised vs. Unsupervised Learning explained
• [18:00] Common algorithms: Regression, Trees, SVMs, KNN, Neural Networks
• [26:00] Feature learning and CNNs in pathology
• [33:00] Pattern detection with unsupervised learning
• [37:00] Annotation strategies: Fully, weakly, self-supervised
• [45:00] Multi-modal AI: Text + image + omics data
• [50:00] No single tool solves everything—toolkit mindset matters

📚 Resource from this episode:

📖 Main Article: "Non-generative artificial intelligence and predictive analytics in medicine"

🔧 Tools & Mentions

• Digital Pathology Trailblazers Book – Free visual eBook
• QPath & V7 Labs – Annotation tools mentioned
• University of Pittsburgh Medical School – Authors’ institution
• Muse Microscopy – Booth 528 at USCAP 2025

This episode is all about real-world AI—how it's already helping us in digital pathology, where it struggles, and how we can use it more responsibly and effectively.

🎧 Listen in to learn why annotation isn’t just a pain—it’s a power move.
🎤 Stay tuned for part 4, where we talk AI + statistics in pathology.

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❗️Is synthetic data trustworthy enough to train AI for patient care? It just might be—and that's what both excites and terrifies me. ❗️

Hey trailblazers! In this episode of the Digital Pathology Podcast, I take you through the second part of our AI in Pathology series—this time, we’re focusing on generative AI and how it’s revolutionizing diagnostics, education, and workflow in our field.

From synthetic H&E slides that could pass for real to multimodal agents that can read your histology images and chat with you about them—yes, really—this is where digital pathology meets the “bleeding edge” of AI development.

We’ll also look at real use cases, a synthetic biobank you can trust, and the biases, hallucinations, and ethical minefields that come along for the ride.

🔍 Highlights with Timestamps

• [00:00] Welcome + Paper Credits
  Kickoff and acknowledgments to the amazing authors behind the paper that inspired this series.
• [00:02] What is Generative AI?
  From rule-based systems to multimodal models and why everyone's suddenly Gen-AI.
• [00:04] AI Tools You Might Already Be Using
  ChatGPT, Claude, Gemini, Llama—and how they’re quietly shaping your workflow.
• [00:07] The Training Process Demystified
  Why it's not magic. From unsupervised learning to reinforcement with human feedback.
• [00:08] Retrieval Augmented Generation (RAG)
  How I use AI to update my book—and how you can use it to mine literature efficiently.
• [00:10] The AI Hierarchy Explained
  Where generative models fit into the bigger AI picture, including foundation models.
• [00:12] Synthetic Data in Practice
  Why training on rare diseases is finally realistic—and why that's a big deal.
• [00:15] Clinical Efficiency with Audio/Video AI
  How dictation and NLP are crushing administrative burden in healthcare.
• [00:18] Multimodal AI Use Case: PathChat
  Yes, I took a pic of my histology earrings—and yes, the AI knew what it was.
• [00:25] Four Types of Generative AI in Medicine
  From simple text generators to complex multi-agent frameworks—examples and use cases.
• [00:37] Hallucinations, Biases, and Top-K Explained
  Understanding why LLMs “lie” and how we can manage it (temperature, top-P, and beyond).
• [00:45] Why Bias is Statistical, Not Personal
  Important talk on systemic bias in LLMs and geographic data interpretation.
• [00:47] Tech Talk: Building a RAG Pipeline
  What it really takes to use RAG effectively (it’s not just plug-and-play).
• [00:48] Risks, Regulations, and Tomorrow
  Benefits are massive, but privacy, cost, and consent still matter.
• [00:52] Course Announcement & Free Gift
  Get 50% off my structured AI in Pathology course using code VAL50.

📚 Resource from this episode

• 🔗 Main Reference Paper: ScienceDirect - Generative AI in Pathology

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Generative vs. Non-Generative AI in Pathology: Why the Difference Matters

If we don’t start defining what kind of AI we’re talking about, we risk letting buzzwords replace real science. 🧠
This is where we begin—at the foundation.

Welcome to the first episode of our 7-part Guided Journey through AI in Pathology, inspired by two must-read articles from Modern Pathology that you’ll want bookmarked forever (links below 👇).

In this episode, I clarify one of the most misunderstood distinctions in artificial intelligence: generative vs. non-generative AI. Spoiler: ChatGPT is not the same kind of AI that segments nuclei or detects tumors.

These differences aren’t just academic—they affect how we train, validate, and regulate tools for diagnostics, research, and clinical care.

🔍 Episode Highlights & Timestamps

[00:00] Welcome to our AI journey! Kicking off the series with a few tech hiccups (as always 😅) and giving context for why these 7 episodes matter.

[02:00] Meet the minds behind the Modern Pathology AI series—Drs. Hanna, Pantanowitz & Rashidi—and why their work inspired this podcast.

[04:00] Core question: What is generative AI? And how is it different from traditional (non-generative) machine learning?

[07:00] Real-world pathology examples: From generating synthetic H&E slides to classifying tumor subtypes—what’s what?

[10:00] Use cases broken down:

• Generative AI: Text-to-image models, LLMs, synthetic training data
• Non-generative AI: Segmentation, classification, detection, clustering

[14:00] Visual metaphors to simplify complexity—think: cake baking vs. quality control inspection 🧁🔍

[17:00] Why understanding these types matters for regulation, validation, and ethical use

[20:00] Real lab examples: When generative models hallucinate vs. when non-generative tools are just wrong

[23:00] What pathologists need to know before choosing or deploying AI tools

[26:00] Sneak peek: How these AI types intersect with statistics, ethics, and regulatory frameworks (in upcoming episodes)

📚 Resource from this Episode

📄 Featured Publications:

1. The Evolution of AI in Medicine: Generative and Non-Generative AI Tools
   🔗 ScienceDirect – Article 1
2. Emerging Use Cases of Generative and Non-Generative AI in Clinical Practice
   🔗 ScienceDirect – Article 2

🛠️ Tools & Terms Mentioned:

• Generative AI Models: ChatGPT, Gemini, Stable Diffusion, GANs
• Non-Generative Models: CNNs, SVMs, Decision Trees, Regression, Clustering
• Key Concepts: Bias, hallucinations, prompt tuning, model performance metrics
• Software Mentions: QPath, Techcyte, LLMs (GPT-4, Claude, LLaMA)

This episode sets the tone for everything we’ll explore in the next six sessions. Whether you’re building models or simply want to understand what AI is actually doing in your lab, start here.

🎧 Ready to become AI-literate in pathology? Hit play and follow along.
Let’s build better tools—with clarity, ethics, and science.

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Will FDA rules disrupt the way we diagnose diseases?

In this episode, I break down a seismic shift in lab medicine: a federal court has vacated the FDA’s controversial rule classifying lab-developed tests (LDTs) as medical devices. This change carries serious implications for innovation, digital pathology, AI-based diagnostics, and small labs across the U.S.

🎧 What You’ll Hear:

• What LDTs are and why they matter for rare diseases and personalized medicine
• Why the FDA rule sparked backlash from the pathology community
• What the court’s decision means for AI algorithms in digital pathology
• What’s next: Will Congress revive the VALID Act?
• How this affects labs, startups, and the future of diagnostic innovation

This podcast is packed with updates every pathology professional should know. 🔍

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You think going digital in pathology just means buying a scanner?

Think again.

In this episode sponsored by Epredia, I sat down with Ryan Davis, Director of Global Business Strategy at Epredia, to talk about what it really takes to implement digital pathology—and why modularity, cytology support, and AI integration are changing the game.

Whether you’re starting your digital journey or scaling up with advanced tech, there’s something in this conversation for you.

🎯 Highlights with Timestamps:

• [00:01:00] Who is Ryan Davis? Epredia’s digital transformation story
• [00:04:00] From Thermo Fisher to PHC Group: A legacy of diagnostic solutions
• [00:06:00] The 3DHistech partnership—why collaboration > competition
• [00:10:00] Spotlight: The P1000 scanner and its water immersion feature
• [00:16:00] Modular, fluorescent, and multi-functional scanners explained
• [00:21:00] Interoperability: Integrating with existing AI and IMS systems
• [00:24:00] Global footprint and customer feedback (Radboud, CorePlus, Puerto Rico)
• [00:27:00] Use cases in biopharma and neuroscience
• [00:30:00] AI partners: Aiforia, PaigeAI, IBEX, TechCyte
• [00:34:00] What’s coming: FDA clearance, polarization scanners
• [00:38:00] Community outreach through DPA and global conferences
• [00:40:00] Learn more: Live Labs and webinars at www.epredia.com

📚 Resources from this Episode:

• CorePlus Study: Deployment of Artificial Intelligence Algorithm for 100% QC of Pap Tests
• Epredia Live Labs: www.epredia.com → Events
• AI partners mentioned: Aiforia, PaigeAI, TechCyte, IBEX

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In this episode, I talk with Tiffany Chen, MD, and Ben Cahoon from Techcyte about Fusion, their new digital pathology platform. Fusion integrates clinical and anatomic pathology workflows, AI algorithms, and electronic health records—all into one streamlined experience.

We explore how Fusion simplifies case management, improves diagnostic accuracy, and brings AI-powered pathology into routine practice. Plus, we discuss the importance of open standards, partnerships with Mayo Clinic, and why flexible integration is key for healthcare innovation.

If you’re passionate about digital pathology, AI, and advancing patient care, this is a conversation you don’t want to miss!

✨ Key Highlights
- Introduction of Techcyte’s Fusion platform: bridging clinical and anatomic pathology workflows
- How Fusion integrates AI, EHRs, and LIS systems using open standards (FHIR, DICOM, HL7)
- Collaborations with Mayo Clinic and BD for scalable global deployment
- AI marketplace support: Fusion enables the integration of internal, partner, and institutional AI models
- Impact of AI on cytology workflows and pathology screening
- Flexibility for lab-driven or PACS-driven workflows
- Future plans: Subspecialty-focused AI enhancements and smart synoptic reporting integration
- The importance of interoperability and data standardization for healthcare AI

This Episode's Resources:

Techcyte Fusion Platform: https://techcyte.com/fusion/

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Why do so many digital pathology tools stall before they ever reach patients?

In this USCAP 2025 special sponsored by Muse Microscopy, I talk with Esther Abels, founder of SolarisRTC, regulatory strategist, and the force behind the first FDA-cleared whole slide imaging system.

We break down what startups and established companies must do from day one to succeedbin getting their devices through the FDA. Hint: regulatory strategy isn’t a final step—it’s your starting line.

🧠 What You’ll Learn:

• [00:01:00] Why regulatory planning must start at inception
• [00:03:00] How Esther helped Philips get the first scanner FDA cleared
• [00:05:00] Clinical study design, documentation, and risk strategy
• [00:08:00] Timeline expectations for clearance and review
• [00:10:00] The role of consultants vs. internal regulatory teams
• [00:13:00] Using meta-analysis, synthetic data, and publications
• [00:15:00] How the DPA is driving system decoupling & AI regulatory clarity

🎧 Tune in to learn how to build compliance into innovation—from tissue imaging to AI-powered diagnostics.

#DigitalPathology #RegulatoryStrategy #FDAApproval #ClinicalInnovation #PathologyTools #MUSEmiscroscopy #sponsored #USCAP2025

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Can you still call it “digital transformation” if you’re scanning slides and still tethered to glass?

This special episode, recorded at USCAP and sponsored by MUSE Microscopy, features Dr. Robert Osamura from Japan. We explore how digital pathology is being implemented across Japanese hospitals, how regulations shape adoption, and where AI and tools like MUSE could fit in a geographically complex healthcare system. We also discuss the real-world utility of direct-to-digital tools for intraoperative diagnostics and how AI is changing confidence, not replacing pathologists.

👉 Tune in for an insightful conversation bridging tradition and innovation in digital pathology.

⏱ Highlights with Timestamps

• [00:00] Starting digital pathology with glass is still required
• [02:00] Dr. Osamura’s current practice in a community hospital near Tokyo
• [03:30] Everyday use of digital pathology for biopsy cases
• [04:30] Technical and logistical advantages of digital images
• [05:45] How Japan’s island geography benefits from digital workflows
• [07:15] Government regulations: dual review (glass + digital)
• [08:40] Adoption challenges and culture around microscopes
• [10:00] The psychological shift away from glass reliance
• [11:30] Potential use cases for MUSE technology in Japan
• [13:20] Direct-to-digital for frozen sections and rapid diagnosis
• [15:00] Hospital-to-hospital collaborations in Japan
• [16:30] Reimbursement and scanner costs as limiting factors
• [17:45] AI as a confidence enhancer, not a replacement
• [18:45] Future of pathology as more patient-centered

Episode Resources:

https://musemicroscopy.com/

sponsored #musemicroscopy #USCAP

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Why are so many pathologists still afraid of going digital?

In this USCAP special episode sponsored by Muse Microscopy, I talk with Dr. Sarah Dry, Chair of Pathology and Laboratory Medicine at UCLA, about real-world adoption, AI fear, and how change is best managed when it's people-led.

From her early digital research lab in 2007 to pioneering innovative workflows at UCLA today, Dr. Dry knows how direct-to-digital imaging and AI can enhance, not replace, our work.

🧠 Key Takeaways:

• [00:01:00] Fear of change and pathologist pushback
• [00:03:00] Direct-to-digital imaging & frozen sectios
• [00:05:00] Redefining histotech roles amid tech shortages
• [00:08:00] CAP standards, tissue storage & digital-first futures
• [00:12:00] Specialty-specific barriers to adoption
• [00:15:00] Why AI is a pathologist extender, not a threat
• [00:18:00] What AI can automate—cell counts, GI screenings & reports
• [00:20:00] Managing change and getting early adopters onboard

🎧 Tune in to hear how digital pathology becomes scalable, without losing the people at its core.

DigitalPathology #AIinPathology #ChangeManagement #PathologistLeadership #WomenInSTEM

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Why is digital pathology progressing faster in some parts of the world than others?

In this international episode sponsored by Muse Microscopy, I sit down with Junya Fukuoka and Norman Zerbe—presidents of the Asian and European Societies of Digital Pathology—to unpack how cultural, regulatory, and infrastructural forces are shaping progress differently across continents.

From direct-to-digital tissue imaging considered an alternative to frozen sections in Asia, to legal hurdles in Europe, we discuss what’s advancing adoption—and what’s still holding it back.

🧠 Key Takeaways:

• [00:01:00] Direct-to-digital tissue imaging and frozen sections
• [00:03:00] Glass-free pathology and multimodal imaging potential
• [00:04:00] Legal and regulatory challenges in Europe
• [00:05:00] Innovation through low-resource workarounds in Asia
• [00:07:00] Building cross-continental collaboration between societies
• [00:08:00] The rise of digital pathology superstars across Asia
• [00:09:00] Upcoming European and Asian conferences and initiatives

🎧 Tune in to hear how Asia and Europe are reimagining pathology—one regulation, one society, and one scanner at a time.

Event Details:

21st European Congress on Digital Pathology

2nd Annual Congress of the Asian Society of Digital Pathology 2025

#DigitalPathology #GlobalDiagnostics #AsianSocietyPathology #EuropeanPathology #DirectToDigital

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At the last Pathology Visions 2024, I sat down with Imogen Fitt of Signify Research and Nick Best from Pathology News for a candid, energetic recap of what’s really shaping the future of digital pathology.

We discuss how two pathologists drove digital pathology adoption in their lab, the reality behind radiology partnerships, the cautionary tale of AI burnout, and how the Technology Buyer’s Guide helps pathologists navigate endless scanner options. From standardization and DICOM to staffing crises, remote workflows, and even Meta glasses—we covered it all.

🧠 Key Moments:

• [00:01:00] Radiology’s growing influence on pathology strategy
• [00:03:00] AI burnout, buzz fatigue & managing adoption expectations
• [00:06:00] Flexibility & staffing: How 2 pathologists drove digital adoption
• [00:08:00] Virtual staining & next-gen imaging excitement
• [00:10:00] Technology Buyer’s Guide: Compare solutions by criteria
• [00:12:00] The “train station” metaphor of the digital pathology journey
• [00:15:00] Wearable AI assistants, Meta glasses & augmented diagnostics
• [00:18:00] Why DICOM will win the standardization race
• [00:21:00] Looking ahead to Pathology Visions 2025 in San Diego

🎧 Tune in to hear how strategy, storytelling, and community are moving the field forward—one scanner, slide, and conversation at a time.

Episode Resources:

• Pathology News Website
• Technology Buyers Guide
• Signify Research Website

DigitalPathology #PathologyVisions #AIinHealthcare #DICOM #VendorSelection

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“It used to take 40 minutes, now it takes 15” - this is what Dr. Alae Kawam said about her AI-powered prostate biopsy evaluation workflow.

In this energizing episode of the Digital Pathology Podcast recorded at PathVisions 2024, Dr. Alae Kawam joins me to reflect on where pathology is headed—from AI-assisted prostate diagnostics to direct-to-digital imaging and beyond. Together, we unpack what’s working, what still feels clunky, and why standardization, staffing flexibility, and smarter AI are critical to the next phase of pathology adoption.

🧠 Key Highlights:

• [00:01:00] Foundation models finally reach real-world pathology
• [00:02:00] Direct-to-digital imaging vs. glass hesitation
• [00:03:00] AI in prostate diagnosis: Gleason grading + time saved
• [00:04:00] The “eyeball method” vs. reproducible AI precision
• [00:05:00] DICOM and radiology’s standardization lessons
• [00:06:00] The ROI debate: business, operations & wellness
• [00:08:00] Staffing retention driving real-world digitization
• [00:09:00] New paths to part-time work with digital options
• [00:10:00] Networking, community & raising the online voice of pathology

DigitalPathology #AIinDiagnostics #PathologyWorkflow #DirectToDigital

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What does it take to build a digital pathology movement across the most diverse region on Earth?

In this episode of the Digital Pathology Podcast, I’m joined by Dr. Junya Fukuoka, practicing pathologist, educator, and founder of the Asian Society of Digital Pathology (ASDP).

From Japan to India, Saudi Arabia to South Korea, Asia’s digital pathology adoption is growing rapidly—and Dr. Fukuoka is helping lead the charge.

We talk about why digital access, multilingual support, and patient advocacy are central to pathology adoption across Asia’s diverse regions.

We also explore what it means to “skip” a step in tech, and why static images and direct-to-digital imaging may be Asia’s most powerful tools.

🧠 Key Topics Covered:

• [00:01:00] Founding ASDP and the need for regional unification
• [00:03:00] Digital pathology in Japan: research, practice & education
• [00:05:00] Patient visibility, advocacy & the gateway to treatment
• [00:08:00] The speed of diagnosis when pathology goes digital
• [00:10:00] AI’s role in standardizing immunohistochemistry scoring
• [00:13:00] Diversity in Asia and the power of real-time translation
• [00:17:00] Static image pathology and skipping scanner dependence
• [00:21:00] Stats: 400 attendees, 45 vendors, 29 countries at ASDP
• [00:24:00] Invitation to the 2025 ASDP Congress in Mumbai, India

🎧 Listen now to learn how ASDP is empowering digital pathology with community, innovation, and inclusivity.

Episode Resources:
🔗 Join ASDP: https://asdp.ai

DigitalPathology #ASDP #PathologyInnovation #GlobalHealth #AsiaInTech

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In this episode, I’m joined by Dr. Hamid Tizhoosh, professor of biomedical informatics at the Mayo Clinic, to unravel what’s truly holding back AI in healthcare, especially pathology.

From the myths of general-purpose foundation models to the missing link of data availability, this conversation explores the technical and ethical realities of deploying AI that’s accurate, consistent, lean, fast, and robust.

📌 Topics We Cover

• [00:01:00] The five essential qualities AI must meet to be usable
• [00:04:00] Why foundation models often fail in histopathology
• [00:08:00] What “graceful failure” looks like in AI for diagnostics
• [00:13:00] The problem with data silos and missing clinical records
• [00:22:00] Why specialization in AI models is non-negotiable
• [00:34:00] The role of Retrieval Augmented Generation (RAG)
• [00:43:00] How transformer models broke away from brain mimicry
• [00:50:00] Academic dishonesty, publication pressure & bias
• [01:04:00] Decentralized AI and why it won’t solve big problems
• [01:12:00] Data diversity, disparity, and the realities of healthcare bias

🔍 If you’ve ever wondered why AI tools stall in real-world pathology labs, this episode breaks it down with honesty, clarity, and vision.

THIS EPISODE’S RESOURCES:

• Foundation Models and Information Retrieval in Digital Pathology (Paper)
• Foundation Models and Information Retrieval in Digital Pathology (Video)
• This episode on YouTube

DigitalPathology #AIinMedicine #ClinicalAI #PathologyInnovation #BiasInAI

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In this episode of the Digital Pathology Podcast, I sit down with Dr. Yuri Nikiforov, founder of the World Tumor Registry, to explore how this global, open-access whole slide image platform contributes to cancer diagnostics, education, and research.

We talk about how the registry allows pathologists, researchers, and patients to view curated whole-slide images from around the world, starting with thyroid tumors and expanding into other cancers like breast and lung.

Learn how AI, molecular diagnostics, and editorial curation come together to build a truly global pathology tool that’s free for everyone, forever.

🧠 Key Highlights:

• [00:00:00] What the World Tumor Registry is and why it was created
• [00:03:00] How it works: curated, high-quality digital slides from across the globe
• [00:06:00] Interactive diagnostic tools for pathologists and students
• [00:10:00] Contributor guidelines and editorial board responsibilities
• [00:15:00] Using the platform as a patient, clinician, or researcher
• [00:24:00] The role of AI and future treatment-focused video add-ons
• [00:34:00] Platform design, data standards, and free access for all
• [00:42:00] Why this global database is a breakthrough for education and equity

🎧 Listen now to learn how you can contribute, explore rare cases, or use the platform to educate patients, students, and even yourself.

📌 Explore the Registry or Donate:
https://worldtumorregistry.org

DigitalPathology #WorldTumorRegistry #GlobalCancerCare #PathologyEducation #OpenAccessData

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In this episode of the Digital Pathology Podcast, I explore the ethical and bias considerations in AI and machine learning through the lens of pathology. This is part six of our special seven-part series based on the landmark Modern Pathology review co-authored by the UPMC group, including Matthew Hanna, Liam Pantanowitz, and Hooman Rashidi.

From data bias and algorithmic bias to labeling, sampling, and representation issues, I break down where biases in AI can arise—and what we, as medical data stewards, must do to recognize, mitigate, and avoid them.

🔬 Key Topics Covered:

• [00:00:00] Introduction and post-USCAP 2025 reflections
• [00:03:00] Overview of AI and ethics paper from Modern Pathology
• [00:06:00] What it means to be a “data steward” in pathology
• [00:08:00] Core ethical principles: autonomy, beneficence, justice & more
• [00:13:00] Types of bias in AI systems: data, sampling, algorithmic, labeling
• [00:22:00] Temporal and feedback loop bias examples in pathology
• [00:29:00] FDA involvement and global guidelines for ethical AI
• [00:34:00] Bias mitigation: from diverse datasets to ongoing monitoring
• [00:43:00] The FAIR principles for responsible data use
• [00:49:00] AI development & reporting frameworks: QUADAS, CONSORT, STARD

🩺 Why This Episode Matters:
If we want to deploy AI ethically and reliably in pathology, we must check our bias—not just once, but at every stage of AI development. This episode gives you practical tools, frameworks, and principles for building responsible AI workflows from the ground up.

🎧 Listen now and become a more conscious and capable digital pathology data steward.

👉 Get the Paper here: Ethical and Bias Considerations in Artificial Intelligence/Machine Learning

📘 Explore more on this topic: https://digitalpathologyplace.com

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USCAP 2025 Daily Update – Day 4 with Dr. Aleksandra Zuraw

It’s the final day of USCAP 2025, and in this episode of the Digital Pathology Podcast, I’m sharing personal moments, spontaneous tech wins (and fails), and meaningful conversations with some of the most forward-thinking voices in digital pathology.

From running around with mics and misplaced tripods to interviewing Dr. Dry and Dr. Ozumura, this episode captures both the spirit of innovation and the real-world challenges of advancing digital workflows—especially in environments where regulations still lag behind.

🔬 Key Topics Covered:

• [00:00:00] Morning mishaps, behind-the-scenes livestream chaos
• [00:02:00] Meeting international listeners and the Korean heart selfie tip 💙
• [00:03:00] MUSE Booth podcast recordings with Dr. Dry (UCLA)
• [00:05:00] Change management, leadership, and building digital culture
• [00:06:00] Dr. Ozumura's dual-mode pathology workflow in Japan
• [00:08:00] Digital pathology for remote areas and island-based diagnostics
• [00:10:00] What's new at vendor booths? Launching a vendor highlight roundup
• [00:12:00] Interoperability, collaboration, and the growing presence of digital
• [00:14:00] Reflections on the final day, networking, and the future of pathology

🩺 Why This Episode Matters:
Digital pathology is no longer an add-on—it’s the foundation of future workflows. Day 4 at USCAP showed how global leaders, industry partners, and early adopters are all rallying behind the need for interoperability, real-time imaging, and inclusive innovation. Whether it’s Japan’s regulatory balancing act or the power of leadership at institutions like UCLA, this episode is packed with perspective.

🎧 Listen now and close out USCAP 2025 with insights, laughs, and inspiration.

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USCAP 2025 Daily Update – Day 3 Recap with Dr. Aleksandra Zuraw

In this episode of the Digital Pathology Podcast, I bring you Day 3 insights live from USCAP 2025—from moderating the MUSE panel on slide-free imaging to exploring regulatory strategies, tech innovations, and collaborations across the digital pathology community.

Get an inside look at how direct-to-digital pathology is transforming workflows, how companies like Techcyte are streamlining AI applications, and why regulatory strategy is as crucial as your scanning tech.

🔬 Key Topics Covered:

• [00:00:00] Behind the scenes: MUSE panel highlights and audience turnout
• [00:02:00] Slide-free imaging and concordance studies with Dr. Levenson and Dr. Rao
• [00:03:00] Regulatory insights from Esther Abels on digital pathology FDA strategy
• [00:05:00] Poster struggles, TikTok pathology posters, and missed moments
• [00:07:00] Techcyte’s new Fusion platform and use across cellular imaging departments
• [00:08:00] On-the-floor interviews with thought leaders and innovators
• [00:10:00] Where does glassless imaging fit into your workflow?
• [00:12:00] Collaboration opportunities and global connections at the MUSE booth
• [00:13:00] Final thoughts on community, education, and digital pathology’s future

🩺 Why This Episode Matters:
Day 3 was packed with inspiration, breakthroughs, and powerful conversations. Whether you’re navigating regulatory approval, researching AI integration, or figuring out how slide-free imaging fits into your diagnostic flow, this update offers real-world insight from experts shaping the future of pathology.

🎧 Listen now for highlights, reflections, and what’s next at USCAP 2025!

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USCAP 2025 Daily Update – Day 2 with Dr. Aleksandra Zuraw

Welcome back to the Digital Pathology Podcast live from USCAP 2025! On Day 2, I dive into the momentum building across the conference—covering major trends, tech insights, and how digital pathology is no longer just a topic, but a critical tool used to deliver sessions and share knowledge.

From vendor-driven presentations to real-world applications of AI and slide-free technology, this episode explores how digital workflows are being integrated into education, diagnostics, and collaboration on a global scale.

🔬 Key Topics Covered:

• [00:00:00] Day 2 kickoff and society meetings at USCAP
• [00:01:00] Exploring digital talks via touchscreen and vendor-driven sessions
• [00:02:00] How digital tech is embedded into conference presentations
• [00:03:00] Preview of the MUSE Exhibitor Seminar and QR registration
• [00:04:00] Meet the MUSE Panelists: Dr. Richard Levenson, Dr. Rao, and Dr. Jeff Edwards
• [00:06:00] Challenges of digital pathology integration in institutions
• [00:08:00] Conversations about low-cost digital pathology solutions
• [00:09:00] Asian Society of Digital Pathology and regional scanning hubs
• [00:10:00] Updates from PathPresenter, PathAI, and Diaagnexia
• [00:12:00] Poster hall builds, upcoming highlights, and short content plans
• [00:13:00] Call for podcast guests and global collaboration in pathology

🩺 Why This Episode Matters:
Day 2 at USCAP proves that digital pathology is more than a buzzword—it's essential infrastructure for modern diagnostics, education, and global collaboration. From slide-free imaging to low-resource adaptations, we discuss how technology is not only scaling workflows but also making pathology more accessible worldwide.

🎧 Listen to the full episode now and join me as we explore the digital heartbeat of USCAP 2025!

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USCAP 2025 Daily Update – Day 1 Highlights from Dr. Aleksandra Zuraw

Welcome to the first live daily update from USCAP 2025, recorded straight from the conference floor by Dr. Aleksandra Zuraw, your host at Digital Pathology Place. In this episode, Aleks shares behind-the-scenes moments, exciting vendor previews, and key updates as the world’s largest pathology meeting kicks off.

🔬 Key Topics Covered:

• [00:00:00] Welcome and What to Expect at USCAP 2025
• [00:01:00] Media, Exhibitor, and Partner Badges – Working with MUSE, Techcyte & Roche
• [00:02:00] Behind-the-Scenes Glimpse of Booth Setups and Event Logistics
• [00:03:00] Highlights on Digital Pathology Representation Across Exhibitors
• [00:04:00] MUSE Microscopy’s Role and Digital Imaging Focus at the Event
• [00:05:00] Upcoming MUSE Exhibitor Seminar Details
• [00:06:00] Call for Podcast Guests, Poster Presenters, and Collaborators
• [00:07:00] The App, Community Engagement, and First-Day Observations
• [00:08:00] Final Thoughts and Invitation to Connect at USCAP

🩺 Why This Episode Matters:
This isn’t just a recap—it’s a front-row pass to what’s shaping up to be the most exciting year yet for digital pathology at USCAP. From vendor collaborations to hands-on event involvement, you’ll hear how AI, whole-slide imaging, and direct-to-digital technology are being showcased and discussed across the floor.

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In this episode of the Digital Pathology Podcast, I sit down with Matthew Nuñez, CEO of MUSE Microscopy, to discuss the groundbreaking advancements in direct-to-digital imaging in pathology. Traditional pathology workflows rely on glass slides, formalin fixation, and time-consuming processing steps. But what if we could skip the slide entirely and go straight to digital?

🔬 Key Topics Covered:

• [00:00:00] The Challenges of Traditional Pathology Workflows
• [00:01:00] Introducing MUSE: The First Direct-to-Digital Pathology Imager
• [00:02:00] How MUSE Captures Whole Tissue Images Without Slides
• [00:04:00] The Science Behind Microscopy with UV Light Surface Excitation
• [00:06:00] Eliminating Tissue Processing Delays with Real-Time Imaging
• [00:08:00] How Direct-to-Digital Imaging Improves Speed & Diagnostic Accuracy
• [00:10:00] Applications in Pathology: From Clinical Use to Organ Transplants
• [00:12:00] How MUSE Supports Remote and Mobile Pathology Clinics
• [00:15:00] Bridging the Gap Between Veterinary and Human Pathology
• [00:18:00] The Future of Pathology: Faster Diagnoses, Better Patient Care

🩺 Why This Episode Matters:
Pathology is the gateway to diagnosis and patient care—but traditional workflows create delays, inefficiencies, and logistical challenges. With direct-to-digital imaging, we can eliminate glass slides, reduce errors, and enable real-time diagnostics. In this conversation, Matthew Nuñez explains how MUSE is transforming pathology by bringing AI-powered imaging directly to the tissue, skipping the slide, and making diagnoses faster than ever before.

🚀 What’s Next?
This disruptive technology is paving the way for on-site pathology, remote consultations, and real-time patient interaction. If you're attending USCAP 2025, make sure to visit the MUSE booth and witness direct-to-digital imaging in action.

📢 Can't attend in person? Join the USCAP 2025 online experience on March 24, 2025, from 5:30 to 7:00 PM EST!

REGISTER HERE

ℹ️ This Episode's Resources:

• Muse Microscopy website
• This episode on YouTube
• Muse Presentation at USCAP livestream - REGISTER NOW

DigitalPathology #AIinHealthcare #PathologyInnovation #DirectToDigital

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How Can Digital Pathology Workflows Stay Compliant and Efficient?

In this episode of the Digital Pathology Podcast, I sit down with Scott Randall, Senior Application Specialist at Hamamatsu (Hamamatsu NanoZoomer), and Amanda Coble, Senior Director of Product for Proscia (Proscia’s Website), to discuss the critical role of compliance, interoperability, and efficiency in digital pathology workflows.

🔬 Key Topics Covered:

• [00:00:00] Introduction and Initial Challenges in Digital Pathology Compliance
• [00:01:00] Guest Introductions: Meet Scott and Amanda
• [00:03:00] Hamamatsu’s FDA Clearance Journey: Lessons Learned
• [00:06:00] Proscia’s Path to FDA Clearance and Partnering with Hamamatsu
• [00:10:00] How Digital Pathology Systems Maintain Compliance
• [00:14:00] The Advantages of Open Architecture in Digital Pathology
• [00:18:00] Overcoming Challenges in Slide Scanning and Logistics
• [00:22:00] Phases of Digital Pathology Adoption and Future-Proofing Labs
• [00:27:00] The Role of AI and Automation in Digital Pathology Workflows
• [00:32:00] Advice for Vendors Looking to Build Compliant Digital Pathology Solutions
• [00:38:00] Final Thoughts: The Future of Interoperability and Compliance

🩺 Why This Episode Matters:
Regulatory approval in digital pathology isn’t just about scanning slides—it’s about building a seamless, interoperable workflow that ensures accuracy, efficiency, and compliance. Hamamatsu and Proscia were among the first companies to successfully achieve FDA clearance for their integrated solutions, setting the stage for future innovations in AI-powered digital pathology.

Episode Resources:

• Learn more about Hamamatsu’s NanoZoomer series: Hamamatsu NanoZoomer
• Discover Proscia’s AI-driven pathology software: Proscia’s Website
• Read the DPP Blog on selecting the right whole-slide scanner: Choosing the Right Whole-Slide Scanner for Your Pathology Lab

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What if we could skip glass slides altogether and go straight from fresh tissue to digital image? Muse Microscopy's SmartPath device aims to do just that, capturing diagnostic-quality images directly from fresh tissue.

In this episode brought to you by Muse Microscopy, I sit down with Dr. Rao and Dr. Edwards to discuss the insights, challenges, and future of this groundbreaking technology.

We explore its regulatory ramifications, change management in veterinary and human pathology, and financial feasibility.

Tune in to learn why SmartPath could be a game-changer for both pathologists and patients.

00:00 Introduction to SmartPath Technology
00:54 Meet the Experts: Dr. Rao and Dr. Edwards
01:08 FDA Approval and Implementation Plans
01:35 Change Management in Pathology
01:56 Training Pathologists for SmartPath
03:48 Translational Tissue Banking and Clinical Applications
04:29 Impact on Breast Pathology
05:49 Pathologists' Reception and Adoption
14:33 Financial Viability and ROI
19:44 Conclusion and Future Prospects

Links and Resources:

• This episode on YouTube
• Muse Microscopy Website
• SmartPath Device Demo Video

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Transforming Pathology: A Deep Dive into the Muse System

This episode is sponsored by Muse Microscopy.

In this episode, we explore the primary challenge of implementing digital pathology globally—digitizing the analog.

A potential solution is direct-to-digital pathology, exemplified by the MUSE system by Muse Microscopy. This technology eliminates the need for glass slides and manual staining, offering rapid, non-destructive imaging of intact tissue samples.

You will learn about the advantages of Muse, including faster diagnostics, improved data fidelity, and broader accessibility, particularly in remote areas.

Detailed insights into the Muse workflow, imaging techniques, and potential applications in human and veterinary medicine are provided.

Challenges like adoption barriers and regulatory hurdles are also addressed. Join us as we explore how the Muse system is redefining diagnostic workflows and enhancing patient outcomes.

00:00 Introduction to Digital Pathology
00:18 The Hurdle of Digitizing Analog Pathology
00:26 Direct to Digital Pathology: A Game Changer
01:46 Introduction to Muse Microscopy
02:32 How Direct to Digital Pathology Works
03:10 Advantages of Direct to Digital Pathology
04:13 Understanding Muse Technology
05:26 The Digital Pathology Workflow with Muse
14:15 Challenges and Misconceptions
15:38 The Future of Pathology
16:31 Frequently Asked Questions
18:07 Conclusion and Additional Resources
18:54 Behind the Scenes and Final Thoughts

Links and Resources:

• Original blog post on Digital Pathology Place Website and LinkedIn
• YouTube playlist with more information about MUSE
• SmartPath Product presentation from CAP 2024
• Video showing the SmartPath device at the conference booth
• USCAP in Boston - Muse Microscopy booth #528

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In this episode of the Digital Pathology Podcast, I take a deeper dive into Generative AI in Pathology, following the AI in Pathology series published by USCAP. AI has already begun transforming medical diagnostics, but what does Generative AI mean for digital pathology? From synthetic data generation to multimodal AI models, this episode explores the cutting edge of AI’s role in pathology and how it’s evolving to enhance efficiency, accuracy, and patient care.

🔬 Key Topics Covered:

• [00:00:00] Introduction – The Evolution of AI in Pathology
• [00:02:00] Acknowledging the Authors Behind the AI Review Series
• [00:04:00] What Is Generative AI and Why Is It a Game-Changer?
• [00:06:00] The Cambrian Explosion of Generative AI in Medicine
• [00:08:00] Understanding Transformer Architectures and Cloud Computing
• [00:12:00] ChatGPT, DALLE, and Multi-Modal AI: What’s Next?
• [00:18:00] Synthetic Data Generation: A New Era for Pathology Training
• [00:24:00] How AI Can Pass Medical Exams and Assist Pathologists
• [00:30:00] Reducing Bias and Ethical Concerns in AI-Based Diagnostics
• [00:38:00] Real-World Use Cases of AI-Generated Pathology Reports
• [00:45:00] The Future of Generative AI in Digital Pathology

🩺 Why This Episode Matters:
Generative AI is no longer just a concept—it’s already being used to train models, generate high-fidelity pathology images, and assist with diagnostic decision-making. However, challenges remain, from bias in AI models to the need for domain-specific training data. Understanding these factors is essential for pathologists and medical professionals who want to leverage AI responsibly in clinical practice.

🚀 What’s Next?
This episode discusses not just what Generative AI is but how it can reshape pathology workflows and where we’re headed next. If you’re interested in how AI can improve efficiency and accuracy in diagnostics, this is a conversation you don’t want to miss.

🎧 Listen now to explore the future of Generative AI in pathology!

👉 Watch or listen here: https://www.youtube.com/live/hRv9GmMWSjk?si=OEg8gafqRA2M_zlx

DigitalPathology #AIinHealthcare #PathologyInnovation #GenerativeAI

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In this episode of the Digital Pathology Podcast, I explore the evolving role of Generative vs. Non-Generative AI in Medical Diagnostics. As AI continues to transform the medical field, understanding the differences between these two approaches is essential for pathologists, researchers, and healthcare professionals.

We break down the key concepts behind generative AI models (like ChatGPT and image-generation tools) and non-generative AI models (such as traditional machine learning for diagnostic support). I also highlight a groundbreaking seven-part AI review series published in Modern Pathology, which serves as a crucial reference for integrating AI into pathology.

🔬 Key Topics Covered:

• [00:00:00] Introduction and Technical Adjustments
• [00:02:00] Why AI Education in Pathology Is More Important Than Ever
• [00:04:00] Overview of the Modern Pathology AI Review Series
• [00:06:00] Generative vs. Non-Generative AI: What’s the Difference?
• [00:08:00] AI in Pathology: Current Applications and Future Potential
• [00:12:00] Addressing Bias and Ethical Concerns in AI Models
• [00:16:00] How AI Can Improve Accuracy in Medical Imaging
• [00:20:00] The Role of Large Language Models (LLMs) in Pathology
• [00:25:00] Multi-Modal AI: The Future of Integrating Imaging and Text Data
• [00:30:00] Real-World Use Cases and AI-Driven Diagnostics

🩺 Why This Episode Matters:
AI is no longer a futuristic concept—it’s here, and it’s shaping the future of digital pathology and medical diagnostics. In this episode, I break down how AI can enhance accuracy, improve workflow efficiency, and make diagnostic insights more accessible. However, AI models also come with risks, such as bias and interpretability challenges, which we need to address responsibly.

🚀 Take Action:
AI in pathology isn’t just a passing trend—it’s a paradigm shift. Whether you're a pathologist, researcher, or lab professional, this episode will give you the knowledge you need to stay ahead in the era of AI-driven diagnostics.

🎧 Listen now and explore the future of AI in pathology!

👉 Watch it here: https://www.youtube.com/live/Mq4Xwxoq_ok?si=o7bA90BlZff9iI_A

DigitalPathology #AIinHealthcare #PathologyInnovation #GenerativeAI

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In this episode of the Digital Pathology Podcast, I sit down with Dr. Lija Joseph, a pathologist who is redefining patient care by making pathology more accessible and understandable. Traditionally, pathology has been a “behind-the-scenes” specialty, but Dr. Joseph is changing that by directly engaging with patients, showing them their pathology slides, and empowering them with knowledge about their diagnoses.

🔬 Key Topics Covered:

• [00:00:00] Introduction to Patient-Centric Pathology
• [00:01:00] Meet Dr. Lija Joseph: Her Background and Journey
• [00:05:00] The Patient Who Inspired a New Approach
• [00:10:00] Why Patients Need to See Their Pathology Images
• [00:15:00] The Power of Knowledge: How Patient Awareness Improves Care
• [00:20:00] Overcoming Barriers to Patient-Pathologist Communication
• [00:25:00] How Digital Pathology Can Make This Scalable
• [00:30:00] The Role of Technology in Breaking Down Access Barriers
• [00:35:00] Convincing Healthcare Leadership to Support This Initiative
• [00:40:00] How Digital Pathology Can Transform Patient Outcomes

🩺 Why This Matters:
Most patients never meet their pathologists—but should they? Dr. Joseph believes so. She shares powerful stories of how patients who see their own slides gain a deeper understanding of their disease, make better treatment decisions, and experience greater peace of mind.

🚀 How Digital Pathology Can Change the Future:
Dr. Joseph’s approach is innovative, but digital pathology can take it even further. Imagine a world where patients don’t have to visit a hospital to see their biopsy results but can access them remotely through secure digital platforms. This technology has the potential to bridge the gap between patients and their pathologists, improving care and trust.

🎧 Tune in now to learn how pathology can become more patient-focused!

DigitalPathology #PatientCenteredCare #PathologyInnovation #AIinHealthcare

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Welcome to the 21st edition of DigiPath Digest!

In this episode, together with Dr. Aleksandra Zuraw you will review the latest digital pathology abstracts and gain insights into emerging trends in the field.

Discover the promising results of the PSMA PET study for prostate cancer imaging, explore the collaborative open-source platform HistioColAI for enhancing histology image annotation, and learn about AI's role in improving breast cancer detection.

Dive into topics such as the role of AI in renal histology classification, the innovative TrueCam framework for trustworthy AI in pathology, and the latest advancements in digital tools like QuPath for nephropathology.

Stay tuned to elevate your digital pathology game with cutting-edge research and practical applications.

00:00 Introduction to DigiPath Digest #21
01:22 PSMA PET in Prostate Cancer
06:49 HistoColAI: Collaborative Digital Histology
12:34 AI in Mammogram Analysis
17:21 Blood-Brain Barrier Organoids for Drug Testing
22:02 Trustworthy AI in Lung Cancer Diagnosis
30:09 QuPath for Nephropathology
35:30 AI Predicts Endocrine Response in Breast Cancer
40:04 Comprehensive Classification of Renal Histologic Types
45:02 Conclusion and Viewer Engagement

Links and Resources:

• Subscribe to Digital Pathology Podcast on YouTube
• Free E-book "Pathology 101"
• YouTube (unedited) version of this episode
• Try Perplexity with my referral link
• My new page built with Perplexity
• HistoColAI Github Page

Publications Discussed Today:📰 Can PSMA PET detect intratumour heterogeneity in histological PSMA expression of primary prostate cancer? Analysis of [68Ga]Ga-PSMA-11 and [18F]PSMA-1007

📰 HistoColAi: An open-source web platform for collaborative digital histology image annotation with AI-driven predictive integration

📰 Advanced tissue technologies of blood-brain barrier organoids as high throughput toxicity readouts in drug development

📰 Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images

📰 GNCnn: A QuPath extension for glomerulosclerosis and glomerulonephritis characterization based on deep learning

📰 Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer

📰 Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic ty

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In this episode of the Digital Pathology Podcast, you will learn about cytology's entrance into the digital pathology space, including successful AI and scanner implementations.

We cover AI's role in rapid on-site evaluation for lung cancer and share insights on a looming prostate cancer surge and how digital pathology and AI can help. I

You will also listen to a live demo of me using an AI assistant to decode a scientific paper in real-time. Tune in to stay on top of the digital pathology research in 2025!

00:00 Welcome to DigiPath Digest
00:53 Introduction and New Year Greetings
01:41 Diving into DigiPath Digest
01:44 AI in Respiratory Cytology
06:11 The Role of AI in Pathology
09:49 Multi-Omics and AI
11:28 Radiomics and Pathomics
14:44 Live Q&A and Future Plans
20:09 Prostate Cancer Tsunami
22:34 Thyroid Cytology and Live AI-Assistant demo
31:07 Conclusion and the option to send texts :)

Links and Resources:

• Subscribe to Digital Pathology Podcast on YouTube
• Free E-book "Pathology 101"
• YouTube (unedited) version of this episode
• Try Perplexity with my referral link
• My new page built with Perplexity

**Publications Discussed Today:

📝** Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology

📝 Advancing precision medicine: the transformative role of artificial intelligence in immunogenomics, radiomics, and pathomics for biomarker discovery and immunotherapy optimization

📝 A machine learning approach to predict HPV positivity of oropharyngeal squamous cell carcinoma

📝 The uropathologist of the future: getting ready with intelligence for the prostate cancer tsunami

📝 Artificial Intelligence and Whole Slide Imaging Assist in Thyroid Indeterminate Cytology: A Systematic Review

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In this episode, I’m joined by Dr. Giovanni Lujan, Nick Best, and Dr. Alae Kawam to explore a topic that hits close to home for many of us in digital pathology: why do so few pathologists attend digital pathology conferences? We delve into the barriers, opportunities, and actionable solutions that can help bridge this gap and drive the adoption of digital pathology across the profession.

What You’ll Hear in This Episode:

• Startling Observations (00:02:00): Only 10% of attendees at some digital pathology conferences are pathologists. Why is this the case, and what does it mean for the future of the field?
• Barriers to Adoption (00:04:00): From time constraints to a lack of institutional support, we unpack the key challenges stopping pathologists from going digital.
• Mentorship and Collaboration (00:09:00): How mentorship programs can connect seasoned professionals with early-career pathologists to share knowledge and drive adoption.
• The Role of the Next Generation (00:15:00): Insights into how young pathologists can lead the way with their enthusiasm and fresh perspectives.
• Engaging the Skeptics (00:07:00): Strategies to introduce hesitant or resistant pathologists to the transformative power of digital tools.
• Global Comparisons (00:10:00): What can the U.S. learn from Europe and Asia about accelerating digital pathology adoption?
• Leveraging Leadership and Advocacy (00:19:00): How young pathologists can work with leadership to introduce digital pathology as a standard practice in their organizations.
• The Impact of Digital Pathology on Work-Life Balance (00:20:00): Exploring how remote sign-outs and shorter turnaround times can enhance both patient care and pathologists’ quality of life.
• Making Digital Pathology the Norm (00:24:00): Ideas for shifting digital pathology from a secondary option to the standard of care, addressing common fears and misconceptions.
• Future Plans and Next Steps (00:28:00): A look at actionable steps for advancing digital pathology through podcasts, mentorship, and departmental advocacy.

Key Takeaways:
This conversation isn’t just about identifying challenges—it’s about solutions. We discuss how collaboration, leadership, and individual responsibility can drive meaningful change in digital pathology. Whether you’re a seasoned pathologist or just starting your career, this episode offers inspiration and actionable ideas to make digital pathology a core part of the profession.

Connect With Us:
Share your thoughts on why more pathologists aren’t attending these conferences. What’s holding them back, and what changes would you like to see in your department? Let’s keep this important conversation going!

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Leveraging AI for Deep Insights into Tertiary Lymphoid Structures in Colorectal Cancer

In this episode of the Digital Pathology Podcast, I introduce 'Aleks + AI,' a new experimental series leveraging Google's Notebook LM to delve deeper into scientific literature.

Today's focus is on tertiary lymphoid structures (TLS) and their potential to predict colorectal cancer prognosis. We discuss a study published in the October 2024 issue of Precision Clinical Medicine, exploring different methods of quantifying TLS using digital pathology and AI.

The paper title is: "Comparative analysis of tertiary lymphoid structures for predicting survival of colorectal cancer: a whole-slide images-based study"

The findings highlight TLS density as a reliable predictor of survival and its correlation with immune responses and microsatellite instability. We also touch upon the potential for AI to streamline TLS analysis in clinical settings and the broader implications for personalized medicine. Join us as we dive into the intersection of digital pathology and computer science, featuring insights and commentary from my AI co-hosts, Hema and Toxy.

00:00 Welcome and Introduction
00:45 Introducing the New AI Tool: Notebook LM by Google
01:11 Experimental Series: "Aleks + AI"
02:06 Deep Dive into Tertiary Lymphoid Structures (TLS)
03:18 Understanding TLS and Their Role in Colorectal Cancer
04:20 Quantification Methods and Key Findings
05:02 Implications for Personalized Medicine
09:02 AI in TLS Analysis and Future Prospects
11:00 CMS Classification and TLS Density
12:08 Study Limitations and Future Directions
15:40 Final Thoughts and Wrap-Up
16:28 Feedback and Future Plans

THIS EPISODE'S RESOURCES

• 116: DigiPath Digest #18 | Federated Learning in Pathology. Developing AI Models While Preserving Privacy

PUBLICATION DISCUSSED TODAY

📝 Comparative analysis of tertiary lymphoid structures for predicting survival of colorectal cancer: a whole-slide images-based study
🔗https://academic.oup.com/pcm/article/7/4/pbae030/7826772

📱 Send us a text

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In today's DigiPath Digest, we delve into federated learning, a decentralized approach to AI training that preserves data privacy.

I discuss recent papers from PubMed and share my experiences experimenting with AI tools like Perplexity and Gemini for research efficiency.

You will also get updates on upcoming plans, including leveraging AI to share more podcasts with you.

Did I mention that this is the last livestream of the year as I head to Poland for Christmas? No More DigiPath Digests. We got to number 18 (I overestimated it a bit in the podcast), and you have been instrumental in continuing this series!

Big THANK YOU to all the digital Pathology #TRLBLZRS showing up every Friday morning for this!

Join me as we tackle the nuances of federated learning and its impact on healthcare and pathology.

00:00 Introduction and Greetings
00:18 Today's Topic: Federated Learning
00:57 AI Tools and Updates
04:39 Federated Learning in Detail
08:03 Challenges and Benefits of Federated Learning
11:21 Exploring More Papers and Future Plans
22:53 Wrapping Up and Final Thoughts

Links and Resources:

• Subscribe to Digital Pathology Podcast on YouTube
• Free E-book "Pathology 101"
• YouTube (unedited) version of this episode
• Try Perplexity with my referral link
• My new page built with Perplexity

**Publications Discussed Today:

📝 Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine
🔗https://pubmed.ncbi.nlm.nih.gov/39610333/ 📝 A review on federated learning in computational pathology
🔗**https://pubmed.ncbi.nlm.nih.gov/39582895/

If you enjoyed this episode, please subscribe and leave a review on your podcast listening App!

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This episode features a conversation with Dr. Richard Doughty, Senior Medical Advisor at Aiforia Technologies, whose dual training in veterinary and medical pathology offers a unique perspective on the intersections of these fields. Together, we explore the challenges, opportunities, and innovations shaping digital pathology today.

What You’ll Learn in This Episode:

• [00:00:00] Challenges in Pathology Today
  Discussing the pathologist shortage, delayed diagnoses, and how AI can address these issues.
• [00:03:00] A Pathologist’s Dual Perspective
  Dr. Doughty shares his journey of becoming both a veterinary and medical pathologist, and what this means for pathology innovation.
• [00:08:00] AI’s Role in Pathology
  Insights into how clinician-centric AI tools like Aiforia are designed to improve diagnostic workflows and outcomes.
• [00:14:00] Addressing Challenges in AI Integration
  Strategies for overcoming hurdles like algorithm aversion, skill retention, and fostering trust in AI systems.
• [00:22:00] Preparing Future Pathologists
  The importance of incorporating AI training into residency programs and creating a digitally savvy workforce.
• [00:44:00] How to Implement AI Effectively
  Practical advice on selecting tools, collaborating with providers, and setting realistic goals for AI adoption in practice.

Resources and Links Mentioned:

• Learn More About Aiforia: https://www.aiforia.com/
• Download Digital Pathology 101
• Contact Dr. Richard Doughty: richard.doughty@aiforia.com
• Watch more videos created together with Aiforia: VIDEO PLAYLIST

This episode is supported by Aiforia Technologies, leaders in AI-powered solutions for digital pathology.

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In this episode, I sit down with Dr. Nina Kottler, Associate Chief Medical Officer of Clinical AI at Radiology Partners, to dive into the evolving role of AI in radiology and how it can shape the future of digital pathology. Dr. Kottler shares her unique journey, expertise, and practical frameworks for implementing AI that enhance patient care and streamline diagnostic workflows.

Episode Highlights and Key Moments:

• [00:00:45] Introduction to Dr. Nina Kottler
  Dr. Kottler discusses her background in applied mathematics, her journey into medicine, and her work at Radiology Partners, where she combines clinical practice with AI innovation.
• [00:04:30] Breaking Down Complex Problems in AI
  Nina explains her approach to tackling large clinical challenges by breaking them down into manageable parts, a method that’s essential for developing and optimizing AI solutions.
• [00:08:15] The Role of Data Orchestration
  We dig into “data orchestration” and how ensuring data is aligned with the right AI model is key to producing accurate and reliable clinical outcomes.
• [00:11:45] Life Cycle of an Exam in Radiology
  Nina takes us through each step in the radiology workflow—from the initial patient consultation to reporting—and highlights how AI can streamline and enhance each phase.
• [00:17:00] Evolution of AI Models in Healthcare
  We explore how AI has evolved, from early CAD systems to today’s multimodal and transformer models, and the exciting possibilities they bring to both radiology and pathology.
• [00:23:20] Addressing the Lag in AI Adoption in Healthcare
  We discuss the challenge of keeping up with AI advancements while balancing patient safety, regulatory standards, and the need for reliability in clinical settings.
• [00:27:50] Frameworks for Reducing Variability and Improving Accuracy
  Nina shares actionable frameworks that Radiology Partners uses to reduce variability and improve diagnostic precision—strategies that pathology can learn from.
• [00:32:40] AI in Workflow Optimization: Where It Has Real Impact
  We discuss specific use cases in clinical workflows that show where AI can bring the greatest value, especially in enhancing patient care through optimized processes.
• [00:36:50] The Power of Multimodal AI and Vision-Language Models
  Combining large language models with computer vision is moving diagnostics closer to comprehensive, AI-driven care—a promising development we explore in depth.
• [00:42:15] The Future of Agents in AI
  We dive into the concept of “agents” in AI and how these systems may soon coordinate multiple models for more complex and precise clinical analyses.
• [00:48:10] Where to Learn More about Dr. Nina Kottler’s Work
  Nina shares where you can catch her upcoming talks and presentations, plus resources for staying updated on the latest in AI for radiology and digital pathology.

If you're a pathologist, radiologist, or healthcare professional curious about AI’s impact on diagnostics, this episode is packed with practical guidance on integrating AI into clinical workflows. Join us as we explore how AI is shaping the future of radiology and pathology!

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Welcome back to the DigiPath Digest, fresh from PathVision!

In this episode we will dive into the latest updates from the PathVision conference, covering trends in AI-driven diagnostics, the expansion of digital pathology into primary care, and the exciting new frontier of glassless pathology.

Join me as I recap the highlights of PathVision and the latest updates from the digital pathology literature, including discussions on:

• AI Integration in Pathology: Learn how AI is advancing breast cancer diagnostics with tools like Ki-67 scoring models and multi-label AI for mammography, aimed at reducing unnecessary biopsies.
• Global Health & Digital Microscopy: Hear about innovative projects from Sweden and Finland focused on AI-supported digital microscopy in primary healthcare labs, bringing accessible diagnostics to underserved areas.
• Glassless Pathology with MUSE: Discover how glassless pathology is changing tissue imaging with MUSE (Microscopy with UV Surface Excitation), enabling diagnostics without the need for traditional glass slides. Dr. Zuraw breaks down what this means for future pathology workflows.

Plus, a shout-out to the vendors and partners making these advancements possible, and insights from Dr. Zuraw’s conversations with digital pathology trailblazers from around the globe, including new developments from Asia in digital pathology education and technology.

Timestamps:

• [0:00] PathVision Highlights & Global Attendees
• [5:15] AI in Diagnostic Workflows: Dr. Anil Parwani’s “Pathology Train Ride”
• [12:30] Moving Beyond Narrow AI: Multimodal and Foundational Models
• [18:45] Glassless Pathology: A New Frontier with MUSE Microscopy
• [25:10] Integrating Digital Microscopy in Global Health Labs
• [32:00] Breast Cancer Month: New Advances in AI for Diagnostics
• [42:00] One Health & AI for Disease Detection in Primary Care
• [48:30] Special Interviews: Jun Fukuoka and Asian Society of Digital Pathology

Links and Resources:

• Subscribe to Digital Pathology Podcast on YouTube
• Pathology News
• Signify Research Monthly Recap
• YouTube version of this episode

**Publications Discussed Today:

📝 AI-Supported Digital Microscopy Diagnostics in Primary Health Care Laboratories: Protocol for a Scoping Review
🔗https://pubmed.ncbi.nlm.nih.gov/39486020/ 📝 Ki-67 evaluation using deep-learning model-assisted digital image analysis in breast cancer
🔗https://pubmed.ncbi.nlm.nih.gov/39478421/ 📝A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis
🔗https://pubmed.ncbi.nlm.nih.gov/39477432/ 📝 A comprehensive evaluation of an artificial intelligence based digital pathology to monitor large-scale deworming programs against soil-transmitted helminths: A study protocol
🔗** https://pubmed.ncbi.nlm.nih.gov/39466830/

If you enjoyed this episode, please subscribe and leave a review to stay up-to-date with the latest in digi

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In this episode, I meet with Adam Cole, MD, and Jason Camilletti about how digital pathology transforms the field. Adam, the CEO of TruCore Pathology, and Jason, the CEO of PathNet Labs, share their unique journeys from the military to becoming digital pathology leaders. We explore their experiences, challenges, and innovations in integrating AI and digital tools into their practices.

Key Topics Discussed:

• [00:00:00] Introduction to AI in Pathology
• [00:01:00] Adam and Jason’s Military Backgrounds
• [00:05:00] Adam’s Story of Becoming a Mobile Pathologist
• [00:10:00] The Move to Fully Digital Pathology
• [00:14:30] AI’s Role in Pathology
• [00:20:00] Challenges in Implementing Digital Pathology
• [00:25:00] Improving Patient Outcomes with Digital Tools
• [00:29:00] Digital Pathology’s Impact on Patient Care
• [00:38:00] Using AI for Quantifying Tumor Volume
• [00:40:00] The Role of AI in Enhancing Diagnostics

Adam and Jason emphasize the immense potential of AI in pathology, but also the need for thoughtful integration. The future of pathology lies in using digital tools to provide faster, more accurate diagnoses while maintaining the critical human element. Tune in to learn how AI is reshaping the field and what it means for both pathologists and patients.

THIS EPISODE'S RESOURCES:

• TruCore website
• PathNet Website

OTHER EPISODES YOU MIGHT LIKE:

• The Evolution of Digital Pathology – from Improved Histology Quality to Fair Use of Pathology Data w/ Matthew O. Leavitt, DDx Foundation
• Achieving work-life balance in medicine as a pathologist with digital pathology w/ Todd Randolph, MD

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What does the FDA jurisdiction for LDTs mean for the labs? Do they need to worry? How do they need to change the way they operate?

In this episode, I talk with Dr. Thomas Nifong, a clinical pathologist and VP of CDX operations at Acrovan Therapeutics, about the recent FDA ruling on laboratory-developed tests (LDTs) issued on May 6th, 2024. We discuss the implications of considering LDTs as medical devices, requiring regulation, and explore the authority of FDA versus CLIA. The conversation also covers historical contexts, practical implications of regulatory changes, and the roles of organizations like CAP, ACLA, and AMP in legal challenges against the FDA. We dive into the differences in requirements between CLIA and FDA, New York's alternative approval route, and potential impacts on lab operations and compliance. Join us for an insightful conversation filled with essential information for those in the field of molecular pathology.

00:00 Introduction and Special Guest Announcement
00:24 FDA's New Rule on Laboratory Developed Tests (LDTs)
01:58 Recording the Podcast: A Casual Lunch Conversation
03:47 Understanding FDA's Authority Over Medical Devices
08:07 Disputes and Legal Challenges
12:03 Practical Implications and Industry Reactions
12:47 Understanding FDA's Focus: Safety and Efficacy
14:11 The Role of CMS and Medical Necessity
14:48 Congressional Involvement and Legal Authority
16:06 Impact on Labs and Future LDTs
18:33 Quality Systems and Compliance
20:16 Modifications and Software Updates
21:16 Conclusion and Next Steps

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In this episode, I had a fascinating conversation with Candice Chu, DVM, PhD, DACVP, about how artificial intelligence (AI) is reshaping veterinary diagnostics and education. Candice, a clinical pathologist and educator at Texas A&M, is using AI tools like ChatGPT to improve efficiency in clinical workflows and academic processes. We explored the practical applications of AI, ethical concerns, and its future impact on veterinary medicine.

Key Topics Discussed:

• [00:00:00] Introduction to AI in Veterinary Education and Diagnostics
  I ask Candice how AI is changing veterinary education and diagnostics, and she explains how AI is boosting efficiency in both areas.
• [00:01:00] Candice’s Journey in Veterinary Medicine
  Candice shares her journey from Taiwan to the U.S., her career in veterinary pathology, and becoming an educator at Texas A&M.
• [00:05:00] Custom GPT Model for Clinical Pathology
  Candice describes the development of her custom GPT model for clinical pathology and its role in improving diagnostic efficiency.
• [00:10:00] AI Tools for Academic and Clinical Efficiency
  We talk about how AI tools reduce repetitive tasks, giving professionals more time for critical thinking and decision-making.
• [00:14:30] Ethical Concerns When Using AI in Veterinary Medicine
  Candice emphasizes the ethical responsibility of using AI, highlighting the importance of human judgment in AI-assisted diagnostics.
• [00:20:00] How Veterinary Students Can Leverage AI
  Candice shares tips on how students can use AI to enhance learning, from simplifying research to generating case questions.
• [00:29:00] AI’s Role in Academic Writing and Veterinary Practice
  We discuss how AI tools streamline academic writing and research, and how AI will continue shaping veterinary practice in the future.
• [00:39:00] Critical Thinking and AI in Veterinary Medicine
  Candice and I conclude by discussing how critical thinking and professional responsibility are essential when using AI tools.

Candice highlighted the transformative role AI can play in both veterinary education and diagnostics, improving efficiency while requiring responsible use. While AI tools like ChatGPT offer many benefits, the human element—our critical thinking and judgment—remains crucial in ensuring accurate results and ethical practices.

This episode provides practical insights on how veterinary professionals, educators, and students can harness AI to streamline workflows and improve diagnostic accuracy. Be sure to listen to the full conversation for actionable tips on integrating AI into your practice!

EPISODE RESOURCES:

• About Dr. Candice Chu (Including her social media and achievements)
• Candice's Paper
• Undermind AI
• Youtube Episode of this Episode

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In this episode, Dr. Richard Fox shares how AI is transforming veterinary diagnostics. From his early career to the world of AI, Dr. Fox offers practical insights into the challenges, opportunities, and innovations that AI brings to pathology. Tune in to learn how AI is enhancing workflow efficiency, diagnostic precision, and the future direction of veterinary pathology.

[00:00] Introduction – Introduction to Dr. Richard Fox and his expertise in veterinary pathology and AI.

[03:00] Dr. Fox’s Career Journey – His shift from veterinary practice to pathology and AI.

[08:00] Entering the AI Space – How Dr. Fox became involved in AI, including his work with Aiforia.

[15:00] AI in Diagnostics – AI’s impact on diagnostic workflows and speeding up tasks.

[22:00] Quality Control in AI Models – Ensuring AI model accuracy and the importance of data consistency.

[28:00] AI Model Validation Challenges – Overcoming issues with model validation and retraining.

[35:00] Integrating AI into Workflows – How AI fits into veterinary pathology workflows and practical considerations.

[40:00] Future of AI in Pathology – Predictions on the future trends in AI and on-premises diagnostics.

[50:00] Common Questions About AI – Addressing concerns like AI replacing pathologists and optimizing workflows.

[58:00] Conclusion – Key takeaways and how to get started with AI in veterinary diagnostics.

The Episodes Resources:Contact AiforiaRichard Fox's LinkedIn Profile Richard Fox's Email

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In this 14th episode of DigiPath Digest, I introduce a new course on AI in pathology, designed to help pathologists understand and confidently navigate AI technologies.

The episode focuses on various research studies that highlight the integration and effectiveness of AI in pathology, particularly in colorectal biopsies and kidney transplant biopsies, emphasizing the importance of seamless workflow integration.

You will also learn about challenges in manual assessment of tumor-infiltrating lymphocytes and HER2 expression in breast cancer. I advocate for more consistent and precise AI-driven approaches.

And there an opportunity for a discounted beta test of the new AI course.

00:00 Welcome to DigiPath Digest #14

00:24 New AI Course Announcement

01:51 Deep Learning in Colorectal Biopsies

09:17 AI in Kidney Biopsy Evaluation

16:12 Automated Scoring of Tumor Infiltrating Lymphocytes

24:22 AI for HER2 Expression in Breast Cancer

31:13 Conclusion and Course Details

THIS EPISODE'S RESOURCES

📰 A deep learning approach to case prioritisation of colorectal biopsies
🔗 https://pubmed.ncbi.nlm.nih.gov/39360579/

📰 Galileo-an Artificial Intelligence tool for evaluating pre-implantation kidney biopsies
🔗 https://pubmed.ncbi.nlm.nih.gov/39356416/

📰 Automated scoring methods for quantitative interpretation of Tumour infiltrating lymphocytes (TILs) in breast cancer: a systematic review
🔗 https://pubmed.ncbi.nlm.nih.gov/39350098/

📰 Precision HER2: a comprehensive AI system for accurate and consistent evaluation of HER2 expression in invasive breast Cancer
🔗 https://pubmed.ncbi.nlm.nih.gov/39350085/

▶️ YouTube Version of this Episode:
🔗 https://www.youtube.com/live/jkT8dTxelt4?si=xT6MNH7O4HuUnAN6

📕 Digital Pathology 101 E-book
🔗https://digitalpathology.club/digital-pathology-beginners-guide-notification

🤖 "Pathology's AI Makeover" Online Course 50% OFF
🔗 Let me know that you are interested in LinkedIn (just 10 spots available)

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Good morning, digital pathology trailblazers! Welcome to another exciting exploration of digital pathology and AI. I’m thrilled to have our global community here with us today from so many different time zones. Before we dive into today's content, a quick note: my equipment is being a bit finicky, but that’s life in the digital world!

Integrating Image Analysis with AI

Let's kick off with a recap of some recent updates. Yesterday, I had the privilege of presenting to a mixed group at Cincinnati Children’s Hospital. We discussed AI in image analysis, an essential tool bridging radiology and pathology as these fields rapidly evolve with new technologies like foundation models and large language models. A diverse audience—ranging from radiologists to pathologists—prompted me to adapt my presentation style on the spot. It was a dynamic discussion about the advancements in healthcare that shared perspectives from both sides.

Lymphovascular Invasion: A Case Study

Our first paper today focuses on a deep learning model for identifying lymphovascular invasion (LVI) in lung adenocarcinoma. This significant prognostic factor is crucial for advancing diagnostic consistency and reliability. Unlike broad foundation models, this work engages with dedicated image analysis applications targeting specific diagnostic challenges. The study demonstrated reduced pathologist evaluation time by nearly 17% and even more in complex cases, aligning with previous findings that AI enhances efficiency by around 21%.

AI Collaborations: Human and Veterinary Pathology

Next, we delve into a collaborative effort between human and veterinary pathologists, emphasizing the promise of AI integration in telepathology and digital pathology. These fields are converging to enhance information exchange, teaching, and research. I’m particularly excited about this paper due to my own veterinary pathology background and the potential it offers for both educational and clinical practices.

Spatial Profiling and Immuno-Oncology

We then journey into the intricate landscape of immuno-oncology with a study on PD-1 and PD-L1 in osteosarcoma microenvironments. Utilizing deep learning and multiplex fluorescence immunohistochemistry, researchers highlighted the spatial orchestration of these markers, providing insights into potential immunotherapeutic strategies. This work is an exemplar of how AI can illuminate complex biological landscapes, offering a path for future therapies.

Conclusion

Thank you all for joining this vibrant discussion. Whether you’re tuning in from early morning in Atlanta or late at night in Algeria, your engagement enriches our learning experience. Keep an eye out for more content and upcoming courses designed to unpack these groundbreaking developments in AI and digital pathology.

Until next time, keep blazing trails in digital pathology!

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The episode explores the concept of blind review, a process designed to eliminate hindsight bias by allowing medical experts to evaluate cases without knowing the outcome or the hiring party.

Stephanie Franckewitz, JD, MBA, founder of Blind Review, discusses its application in legal cases, particularly for digital pathology and radiology. By providing an unbiased expert opinion, blind review aids the defense and plaintiff parties in court, increasing the chances of a favorable verdict.

Stephanie outlines her journey from a medical malpractice defense lawyer to starting Blind Review and highlights the potential for digital pathology to revolutionize the legal process, reduce bias, and improve case outcomes.

Collaboration with platforms like PathPresenter enables pathology slides to be reviewed efficiently and effectively within a legal context. This approach benefits both defendants and plaintiffs by ensuring objective evaluations and enhancing the credibility of expert testimonies in trials.

00:00 Introduction to Blind Review
01:19 The Role of Digital Pathology in Legal Cases
02:16 Stephanie Franke Reid's Journey
07:19 Challenges in Traditional Expert Reviews
10:09 Implementing Blind Review in Pathology
18:16 Collaboration with PathPresenter
25:43 Streamlining the Legal Process with Digital Pathology
26:51 Collaborative Tools for Legal Experts
27:20 Path Presenter: A Game Changer for Attorneys
28:17 Understanding Pathology for Juries
29:20 Streamlining Case Preparation with Path Presenter
31:54 Setting Up a Blind Review Process
35:38 The Gold Standard of Blind Review
41:53 Impact of Blind Review on Legal Outcomes
49:49 Empowering Legal and Medical Professionals
54:50 Conclusion and Call to Action - contact Stephanie

THIS EPISODE'S RESOURCES

• Stephanie's LinkedIn Profile
• The Blind Review Website

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In this episode, I celebrate another milestone of the Digital Pathology Place YouTube channel that was achieved thanks to you, my digital pathology trailblazer, reflecting on its journey since its inception in 2019.

I delve into the developments in digital pathology, focusing on the first video I ever published on YouTube about AI in pathology, highlighting trends, tools, and challenges in the field.

The video was based on a presentation I gave on the day I got engaged, so if you want to know the whole story listen in.

I explain key concepts like
- artificial intelligence,
- machine learning, and
- deep learning, and discuss
- How could AI eventually support pathology practice despite current challenges?

00:00 Welcome and AI Co-Host Feedback
00:19 YouTube Monetization Milestone
01:18 Reflecting on the First Video
02:47 Special Day and Personal Story
05:06 Introduction to AI in Pathology
07:26 AI Terminology and Concepts
13:17 Current Status of AI in Pathology
17:33 Challenges and Future of AI in Pathology
22:42 Conclusion and Call to Action
23:30 Updates and Future Plans

THIS EPISODE'S RESOURCES

• The YouTube version of "AI in Pathology" first video
• The updated "Artificial Intelligence in Pathology" video (coming soon in podcast version)

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In this episode of DigiPath Digest you will learn about the development of AI models for glaucoma screening using fundus images, the use of AI in detecting metastatic deposits in colorectal cancer, and leveraging immunofluorescence data to reduce pathologist annotation requirements.

Dr. Aleks also invited two AI Co-hosts and shared personal reflections on AI's role in the industry and invites feedback from listeners on AI-generated content.

00:00 Introduction to the Livestream Disaster
00:24 AI to the Rescue: Enhancing Audio Quality
00:38 Meet the AI Co-Hosts
01:04 Welcome to the Digital Pathology Podcast
01:30 Technical Difficulties and Audience Interaction
02:49 Exploring AI in Veterinary Medicine
04:34 Hybrid Convolutional Neural Network for Glaucoma Screening
07:49 Model for Detecting Metastatic Deposits in Lymph Nodes
11:23 Leveraging Immunofluorescence Data for Lung Tumor Segmentation
18:05 AI-Generated Content and Future Plans
21:37 AI Co-Hosts Take Over
32:42 Conclusion and Audience Feedback

TODAY'S EPISODES RESOURCES
📰 Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images
🔗https://pubmed.ncbi.nlm.nih.gov/39301801/

📰 Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network
🔗https://pubmed.ncbi.nlm.nih.gov/39300922/

📰 Retrosynthetic analysis via deep learning to improve pilomatricoma diagnoses
🔗https://pubmed.ncbi.nlm.nih.gov/39298885/

📰 Obesity-Associated Breast Cancer: Analysis of Risk Factors and Current Clinical Evaluation
🔗 https://pubmed.ncbi.nlm.nih.gov/39287872/

📰 Model for detecting metastatic deposits in lymph nodes of colorectal carcinoma on digital/ non-WSI images
🔗 https://pubmed.ncbi.nlm.nih.gov/39285483/

📰 Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning
🔗 https://pubmed.ncbi.nlm.nih.gov/39284813/

📰 Bayesian Landmark-based Shape Analysis of Tumor Pathology Images
🔗 https://pubmed.ncbi.nlm.nih.gov/39280355/

📰 Globalization of a telepathology network with artificial intelligence applications in Colombia: The GLORIA program study protocol
🔗 https://pubmed.ncbi.nlm.nih.gov/39280257/

📰 Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy
🔗 https://pubmed.ncbi.nlm.nih.gov/39277586/

📰 Sex differences in sociodemographic, clinical, and laboratory variables in childhood asthma: A birth cohort study
🔗 https://pubmed.ncbi.nlm.nih.gov/39019434/

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In this episode of DigiPath Digest, we review the latest AI developments in digital pathology described in the literature. I explore how AI is pushing the boundaries of metastasis detection, breast cancer treatment predictions, lung cancer research trends, and the creation of pathology foundation models.

Episode Breakdown:

• 00:00 – Welcome & Introduction
• 00:36 – Sentinel Node Metastasis Detection: A discussion on the development of an AI model that can detect sentinel node metastasis in melanoma with accuracy comparable to that of pathologists. The model aids in distinguishing between nodal metastasis and intra-nodal nevus, which is crucial for accurate staging in melanoma patients.
• 05:01 – Predicting Breast Cancer Treatment Response: A cross-modal AI model that integrates pathology images and ultrasound data is explored. This model is designed to predict a breast cancer patient’s response to neoadjuvant chemotherapy, providing personalized insights that can guide treatment decisions.
• 09:59 – Global Trends in AI and Lung Cancer Pathology: This section reviews a bibliometric study that analyzed global research trends in AI-based digital pathology for lung cancer over the past two decades. The study highlights the need for increased collaboration between institutions and countries to further AI advancements in this area.
• 13:30 – Pathology Foundation Models: An in-depth look at a new foundation model in pathology, designed to generalize across various diagnostic tasks. This model shows significant promise in cancer diagnosis and prognosis prediction, outperforming traditional deep learning methods by addressing domain shifts across different datasets.
• 20:08 – Domain Shifts in AI Models: A brief discussion on the impact of domain shifts, such as variations in staining protocols and patient populations, on the performance of AI models in pathology. Strategies for mitigating these challenges are highlighted.
• 29:09 – Faster Annotation in Pathology: The episode concludes with a review of a study comparing manual and semi-automated annotation methods. The semi-automated approach significantly reduces the time required for annotating whole slide images, offering a more efficient solution for pathologists.

Resources Mentioned:📰 Sentinel Node Metastasis Detection in Melanoma
🔗 https://pubmed.ncbi.nlm.nih.gov/39238597/

📰 Cross-Modal Deep Learning for Breast Cancer Response
🔗 https://pubmed.ncbi.nlm.nih.gov/39237596/

📰 Global Bibliometric Mapping in Lung Cancer Pathology
🔗 https://pubmed.ncbi.nlm.nih.gov/39233894/

📰 CHIEF Foundation Model for Cancer Diagnosis
🔗 https://pubmed.ncbi.nlm.nih.gov/39232164/

📰 Improving Annotation Processes in Pathology
🔗 https://pubmed.ncbi.nlm.nih.gov/39231887/

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Welcome to the 10th edition of the DigiPath Digest. Today, we discuss essential updates including the free availability of my 'Digital Pathology 101' book and the podcast now accessible on YouTube and YouTube Music. We dive deep into the weekly abstract, focusing on advancements such as sex-specific histopathological models for gliomas, leukocyte identification tools, and automated Gleason grading for prostate cancer. We also explore the potential of SciSpace, an AI tool for interacting with scientific papers. Interspersed with live interaction, we discuss the importance of consistency in histopathological grading and the challenges faced by pathologists. J

00:00 Introduction and Announcements
00:55 Live Interaction and Updates
05:01 Abstract Review: High-Grade Gliomas
11:45 Abstract Review: Leukocyte Identification Tool
13:24 Abstract Review: Gleason Grading in Prostate Cancer
16:31 Abstract Review: HER2 Low Prediction in Breast Cancer
24:01 Event Announcements and Closing Remarks

THIS EPISODES RESOURCES:

📰 Sexually dimorphic computational histopathological signatures prognostic of overall survival in high-grade gliomas via deep learning
🔗https://pubmed.ncbi.nlm.nih.gov/39178259/

📰 A Digital Tool Supporting Pathology Practice and Identifying Leucocytes
🔗https://pubmed.ncbi.nlm.nih.gov/39176939/

📰 Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
🔗https://pubmed.ncbi.nlm.nih.gov/39176576/

📰 Weakly-supervised deep learning models enable HER2-low prediction from H &E stained slides
🔗https://pubmed.ncbi.nlm.nih.gov/39160593/

▶️ YouTube Version of this Episode:
🔗 https://www.youtube.com/live/06QXmwojxDE?si=q59PjGkHbXCUFhwI

📕 Digital Pathology 101 E-book
🔗https://digitalpathology.club/digital-pathology-beginners-guide-notification

Show less

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Today my guest is Danielle Brown, a fellow veterinary pathologist, the General Manager at Charles River Laboratories Reno, Nevada, and a pioneer in the use of image analysis for toxicologic pathology. Together, we explored the ever-evolving role of image analysis in preclinical studies and how it enhances, rather than replaces, the expertise of pathologists.

This conversation is a deep dive into the intersection of pathology and technology, showcasing how image analysis is revolutionizing preclinical research. We also discuss the future of this technology and its implications for the industry.

Join us as we navigate the intricacies of image analysis, share insights on the collaborative process between pathologists and image analysis scientists, and look ahead to the exciting advancements on the horizon.

Key Discussion Points:

• [00:00:00] Introduction and Guest Welcome:
  • Introducing Danielle Brown and her significant contributions to the field of toxicologic pathology.
• [00:02:46] The Role of Image Analysis in Preclinical Drug Development:
  • Why image analysis is crucial for accurate and efficient evaluations in preclinical studies.
• [00:03:23] Challenges and Limitations of Visual Analysis:
  • Discussing the limitations of visual analysis and how image analysis overcomes these challenges.
• [00:08:06] Pathologist and Image Analysis Collaboration:
  • The importance of collaboration between pathologists and image analysis scientists to ensure accurate data interpretation.
• [00:13:00] Efficiency and Cost of Image Analysis vs. Pathologist Scoring:
  • Comparing the efficiency, cost, and consistency between image analysis and traditional pathologist scoring methods.
• [00:15:18] Validation and Qualification of Image Analysis Algorithms:
  • The process of validating image analysis algorithms to ensure they meet regulatory standards in a GLP environment.
• [00:19:54] GLP Compliance and Regulatory Considerations:
  • How Charles River ensures GLP compliance in their image analysis processes, making them suitable for regulatory submissions.
• [00:23:27] Method Development for Specific Stains and Techniques:
  • Approaching projects that require new method development or specialized procedures.
• [00:27:46] Future of Image Analysis in Pathology:
  • Danielle’s insights into the future of image analysis and how emerging technologies will shape the field.

This episode is packed with valuable insights and practical advice for anyone involved in preclinical research or interested in the integration of image analysis in pathology. Danielle’s expertise and our discussion provide a roadmap for leveraging image analysis to increase evaluation efficiency and the granularity of your data.

THIS EPISODE'S RESOURCES:

📄 The paper Aleks and Danielle co-authored: "Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology"

📄 Learn more about GLP-compliant tissue image analysis at Charles River Laboratories.

▶️ Watch the full episode here: Image Analysis Enhances Pathology Evaluation of Preclinical Studies, not Replaces it.

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In this episode, we celebrate the 100th edition of the Digital Pathology Podcast!
Thank you so much for being part of this journey!
You are my Digital Pathology Trailblazers and I prepared a Digital Pathology Trailblazer manifesto for us!

This is the 9th edition of DigiPath Digest, and we are attracting more and more people to this series.

I am also working on a new YouTube digital pathology course and am offering the first 100 enrollments for free in exchange for feedback.

During today's episode, we cover several papers including research on AI for predicting post-operative liver metastasis, validation of AI-based breast cancer risk stratification models, AI applications in clinical microbiology, advances in parasitology diagnostics, AI for retinal assessment, and AI models for detecting microsatellite instability in colorectal cancer.

We also unveil a Digital Pathology Trailblazer manifesto emphasizing the ethos and dedication of the community.

Join us to stay current with literature, advancements, and insights from the fascinating world of digital pathology.

00:00 Introduction and Announcements
00:25 Live Podcast Proposal
01:40 Welcome and Audience Interaction
03:05 Updates and Apologies
06:11 YouTube Course Announcement
07:23 Technical Difficulties and Solutions
10:00 Digital Pathology Club and Vendor Sessions
11:28 First Research Paper Discussion
17:38 Second Research Paper Discussion
20:07 ER Positive and HER2 Negative Patient Subgroup Analysis
20:59 Independent Prognostic Value of StratiPath Breast Solution
21:59 Challenges and Benefits of Image-Based Stratification
22:58 Technical Difficulties and Live Stream Interaction
24:22 Introduction to Paper Number Three: AI in Clinical Microbiology
28:07 AI in Parasitology Screening and Diagnosis
29:30 Physics-Informed AI for Retinal Assessment
33:08 AI for Microsatellite Instability Detection in Colorectal Cancer
36:42 YouTube Course Announcement and Digital Pathology Trailblazer Manifesto
42:25 Celebrating the 100th Episode of the Digital Pathology Podcast

THE ABSTRACTS WE COVERED TODAY📄 A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics.
https://pubmed.ncbi.nlm.nih.gov/39143624/

📄 Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images
https://pubmed.ncbi.nlm.nih.gov/39143539/

📄 Potential roles for artificial intelligence in clinical microbiology from improved diagnostic accuracy to solving the staffing crisis
https://pubmed.ncbi.nlm.nih.gov/39136261/

📄No longer stuck in the past: new advances in artificial intelligence and molecular assays for parasitology screening and diagnosis
https://pubmed.ncbi.nlm.nih.gov/39133581/

📄Physics-informed deep generative learning for quantitative assessment of the retina
https://pubmed.ncbi.nlm.nih.gov/39127778/

📄Artificial Intelligence Models for the Detection of Microsatellite Instability from Whole-Slide Imaging of Colorectal Cancer
https://pubmed.ncbi.nlm.nih.gov/39125481/

▶️ YouTube Version of this Episode
https://www.youtube.com/live/Uwca5rzAtEA?si=Rd8r4LVM1utEKWdt

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In this episode of DigiPath Digest, broadcasting from Poland, we delve into advances in digital pathology, including AI applications in bone marrow evaluation, classification of hematology cells, and the use of synthetic images for data augmentation. Additionally, we review a survey on pathologists' perceptions of ChatGPT and consider the feasibility of GANs for enhancing medical image analysis.

00:00 Welcome and Troubleshooting from Poland
00:21 Live Stream Challenges and Conference Details
02:21 Digital Pathology Podcast Introduction
02:51 Technical Difficulties and Audience Interaction
06:18 Exploring Digital Pathology Papers
06:43 Advances in Bone Marrow Evaluation
09:03 AI in Hematology and Pathology
12:28 Colorectal Cancer Prognostication
19:34 Pan-Cancer Xenograft Repository
25:16 ChatGPT and Pathology Survey
30:55 Synthetic Image Generation in Pathology
36:35 Upcoming Conferences and Courses
42:27 Closing Remarks and Future Plans

THE ABSTRACTS WE COVERED TODAY

📄 Advances in Bone Marrow Evaluation
https://pubmed.ncbi.nlm.nih.gov/39089749/

📄 Digital Imaging and AI Pre-classification in Hematology
https://pubmed.ncbi.nlm.nih.gov/39089746/

📄 Evaluation of CD3 and CD8 T-Cell Immunohistochemistry for Prognostication and Prediction of Benefit From Adjuvant Chemotherapy in Early-Stage Colorectal Cancer Within the QUASAR Trial
https://pubmed.ncbi.nlm.nih.gov/39083705/

📄 A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
A survey analysis of the adoption of large language models among pathologists
https://pubmed.ncbi.nlm.nih.gov/39082680/

📄 Clinical-Grade Validation of an Autofluorescence Virtual Staining System with Human Experts and a Deep Learning System for Prostate Cancer

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Exploring Foundation Models in Digital Pathology: Insights and Tools

In today's DigiPath Digest we talk about the foundation models in pathology.
reviewing abstracts from two notable papers in Nature.

We discuss the high-level overview of these models, including Hamid Tizhoosh's insights on the vast data requirements for developing effective foundational models.

We also explore tools for literature research, comparing PubMed and Undermind.ai, and examine a useful children's book on artificial intelligence :)

The episode features audience interaction and offers updates on digital pathology trends, along with a personal anecdote on the nature of comparison based on a yoga class experience.

00:00 Introduction and Overview
00:16 Foundation Models in Pathology
00:33 Comparing Research Tools
01:03 Live Stream Interaction
01:12 Starting the Podcast
04:51 Foundation Models Explained
05:11 Research and Findings
06:34 Children's Book on AI
08:00 Deep Dive into Foundation Models
14:28 Case Studies and Examples
18:18 Discussion on Data and Models
21:00 Final Thoughts and Questions
26:24 Exploring ToxPath and Foundation Models
27:05 Introduction to Image Repositories
28:36 Using PubMed for Research
30:35 Exploring Undermined Tool
35:42 Comparing PubMed and Undermined
41:00 Final Thoughts and Recommendations

TODAY'S ABSTRACTS & RESOURCES📄 Here are the abstracts reviewed today:

• A visual-language foundation model for computational pathology
• A foundation model for clinical-grade computational pathology and rare cancers detection

▶️ Hamid Tizhoosh's lecture:

• "Foundation Models and Information retrieval in Pathology"

🔧 The tool we tried today

• Undermind (for literature research)

📕 A book we discussed :)

• "ABC of Artificial Intelligence. Baby University"

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The third episode of DigiPath Digest just took place live, but I have an audio version for the listeners.

DigiPath Digest is a review of digital pathology and IA publications abstract review that I host weekly as a live stream (on YouTube, LinkedIn, Facebook etc.)

Here is the video version if you learn more visually

Today the abstracts we discussed centered around innovations in disease detection and prognosis powered by digital pathology and AI.

TIMESTAMPS:

00:00 Welcome and Introduction

00:35 DigiPath Digest Overview

01:14 Engaging with the Audience

06:09 Abstract Review: AI in Liver Fibrosis

11:21 Abstract Review: AI in Prostate Cancer

16:43 Abstract Review: AI in Glioblastoma

23:02 Abstract Review: AI in Red Blood Cell Analysis

28:38 Upcoming Events and Announcements

34:18 Closing Remarks and Future Episodes

TODAY'S ABSTRACTS & RESOURCES:

• AI-based digital pathology provides newer insightsinto lifestyle intervention-induced fibrosisregression in MASLD: An exploratory study
• Artificial intelligence for detection of prostate cancer
  in biopsies during active surveillance
• Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images
• 1 Million Segmented Red Blood Cells With 240 K Classified in 9 Shapes and 47 K Patches of 25 Manual Blood Smears

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In this episode of the People of Pathology Podcast, I had the pleasure of being interviewed by Dennis Stenk. We delved into the fascinating world of digital pathology, focusing on the cultural shift and learning mentality essential for embracing rapid advancements in the field. Our discussion highlighted the importance of open-mindedness, interdisciplinary collaboration, and the practical applications of AI to enhance diagnostic and workflow processes.

I also shared insights from my book, "Digital Pathology 101," a comprehensive resource for beginners and experts. We covered key topics such as regulatory milestones, interoperability, and the evolving role of toxicologic pathology.

Key Topics Discussed:

• Introduction to Learning Mentality (00:00): Exploring the mindset required for adapting to new technologies in digital pathology.
• Welcome to the Digital Pathology Podcast (00:53): Kicking off the conversation with an introduction to the podcast.
• The Journey of Writing Digital Pathology 101 (02:03): Sharing the process and inspiration behind writing the book.
• Balancing Content for Different Readers (05:29): How to make the book accessible and valuable for newcomers and seasoned professionals.
• Digital Pathology Milestones and Surprises (10:27): Highlighting significant achievements and unexpected developments in the field.
• Interoperability in Digital Pathology (15:19): The importance of systems working together seamlessly.
• Collaborating with Vendors (17:29): The benefits and challenges of working with technology providers.
• The Cultural Shift in Digital Pathology (21:47): Emphasizing the need for a cultural change within the pathology community to embrace digital tools fully.
• AI in Pathology: Narrow vs. General AI (27:17): Differentiating between specific AI applications and broader AI capabilities.
• Companion Diagnostics and Personalized Medicine (35:34): The role of digital pathology in advancing personalized treatment plans.
• The Role of Toxicologic Pathology (38:49): Exploring the evolving role of toxicologic pathology in the digital age.
• Conclusion and Final Thoughts (42:37): Wrapping up the discussion with reflections and future outlooks.

This conversation with Dennis was incredibly enriching, shedding light on the multifaceted world of digital pathology and its future. I hope you'll find the episode as insightful and inspiring as I did.

For more insights and to dive deeper into digital pathology, be sure to check out my book "Digital Pathology 101." You can register to get your copy here: Digital Pathology 101 E-book.

Watch the full episode here:

THIS EPISODE'S RESOURCESThe original audio version of Dennis' podcast is here:
🎧 https://peopleofpathology.podbean.com...

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This is the audio version of the DigiPath Digest - Abstract review that I host on YouTube

Here is the video version if you learn more visually

Today I explain what happened with my "beginning of year initiative" to post an audio version of the Digital Pathology Newsletter sent out in an email form.

In a nutshell: I just stopped posting it, you will find out why in this episode.

TIMESTAMPS

00:00 Introduction to DigiPath Digest
00:13 Challenges in Digital Pathology
01:31 Consistency and Sustainability
02:51 Abstract Review Process
04:25 Engaging with the Community
08:31 First Abstract: Molecular Classification of Breast Cancer
14:21 Second Abstract: AI in Breast Cancer Detection
20:53 AI-Assisted Pathology: Time Reduction and Sensitivity Improvement
21:36 Environmental Impact of Digital Pathology
22:23 Technical Difficulties and Viewer Interaction
24:30 French Authorities on Digital Pathology's Environmental Cost
28:45 Cephalometric Analysis: Digital vs. Manual Tracing
31:46 Exploring Undermined.ai for Scientific Research
43:05 Concluding Remarks and Future Plans

TODAY'S ABSTRACTS & RESOURCES

• Molecular Classification of Breast Cancer Using Weakly Supervised Learning (https://pubmed.ncbi.nlm.nih.gov/38938010/)
• Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: the CONFIDENT-B single-center, non-randomized clinical trial (https://pubmed.ncbi.nlm.nih.gov/38937624/)
• [The environmental impact of digital technology and artificial intelligence, in the time of digital pathology] (https://pubmed.ncbi.nlm.nih.gov/38937204/)
• Digital versus Manual Tracing in Cephalometric Analysis: A Systematic Review and Meta-Analysis (https://pubmed.ncbi.nlm.nih.gov/38929786/)

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How can you work remotely as a doctor? Clearly some specialties, give more possibilities to do that than others and pathology is one of them.

In this episode, I talk to Dr. Todd Randolph, a pathologist living the remote pathologist lifestyle.

Dr. Randolph shares his journey into digital pathology, including his background, the evolution of his practice, and the transition to remote work.

We discuss the benefits and challenges of digital pathology, including the importance of pathology and business experience, as well as insights into AI in pathology.

Dr. Randolph also provides advice for those looking to pursue a career in digital pathology and emphasizes the importance of taking initiative and staying informed about the field.

TIMESTAMPS

00:00 Introduction to the Guest: Dr. Todd Randolph

01:05 Todd's Pathology Journey

02:04 Specialization in Pathology

03:55 Transition to Digital Pathology

05:01 Working with Lumea

11:44 Daily Life as a Remote Pathologist

13:36 Challenges and Benefits of Digital Pathology

20:37 Starting a Career in Digital Pathology

24:45 Early Days of Digital Pathology

26:17 Challenges in Digital Pathology Systems

27:46 Exploring Different Digital Pathology Systems

29:03 Impact of Digital Pathology on Work-Life Balance

32:50 Advice for Aspiring Digital Pathologists

41:11 The Role of AI in Digital Pathology

50:40 Regulatory Considerations for AI Tools

54:11 Final Thoughts and Encouragement

THIS EPISODE’S RESOURCES

• Dr. Todd Randolph on LinkedIn
• Lumea’s website
• Digital Diagnostics Summit Registration Link

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In this episode, join me as I speak with Dr. Greg Rose, a retired radiologist who played a key role in the digital transformation of radiology. His journey offers valuable insights and lessons for the digital pathology community.

Key Points Discussed:

• Initial Digital Adoption Challenges: Greg's experience with transitioning from analog to digital, focusing on the challenges related to change management and personality dynamics.
• Digitization Process: How radiology moved from plain film to digital modalities like CT, MRI, and ultrasound, and the steps involved in digitizing these images
• Technical and Political Hurdles: Navigating technical issues, workflow optimization, and dealing with political dynamics within medical institutions.
• Managing Change: Effective strategies for involving senior staff and managing resistance to change.
• AI in Radiology: Current applications of AI in radiology, its potential for pathology, and the legal implications of AI-assisted diagnostics.
• Future Directions: Greg's vision for the future of digital health, including the development of tappable databases and the evolving roles of radiologists and pathologists.

Greg's insights into the digital transformation of radiology provide a valuable perspective for pathologists looking to embrace digital tools and techniques. His experience highlights the importance of managing change, leveraging AI, and improving diagnostic workflows.

THIS EPISODE'S RESOURCES

🔗 Dr. Greg Rose's website

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In this episode Dr. Aleks Zuraw sits down with Mariano De Socarraz, President of CorePlus and member of the board of directors at the Digital Pathology Association.

CorePlus is an anatomic and clinical pathology lab in Puerto Rico that has fully embraced digital pathology and AI.

Key Takeaways

• CorePlus converted to 100% whole slide imaging for primary diagnosis in January 2020, before the pandemic
• They are pioneering the use of AI algorithms from companies like IBEX, AlpenGlow, Artera and TechCyte for cancer detection and precision pathology
• Digital pathology increases efficiency by ~30% and enables benefits like remote reading, better ergonomics, and seamless sharing of cases
• AI helps detect missed lesions, reduce interoperator variability, and eliminate false negatives in prostate biopsies
• The future of pathology will involve predictive and prognostic information generated right from digital slides (histomics)

Making the Digital Transition

Mariano shares how CorePlus, as a technology-forward company, decided in 2018 to fully convert to digital pathology. They took 2019 to prepare, validate their processes following CAP guidelines, and get full buy-in from stakeholders. On January 1st, 2020 they were fully digital.

While acknowledging that glass slides have advantages in simplicity, Mariano believes the benefits of digital pathology for patients and pathologists are too great to ignore. His advice for other practices considering the digital transition:

• Focus on re-engineering your workflows first before choosing scanners
• Plan for IT redundancy to avoid any disruption
• Get full buy-in from your team
• Start focused and build successes in stages

Unlocking the Power of AI

After seeing a press release about UPMC and IBEX using AI to diagnose prostate cancer, CorePlus reached out to partner with them. They became the first site outside the UK to validate and implement IBEX's algorithm, running it on over 9500 cases as a QC tool.

The algorithm was able to alert pathologists to missed lesions in 73 patients that would have otherwise been false negatives. CorePlus has now moved the algorithm to the front-end to pre-screen and triage all prostate cases.

They are also partnering with other AI companies like AlpenGlow, Artera and TechCyte to bring these benefits to breast, GI, cytology and other subspecialties. Mariano sees AI generating predictive and prognostic insights right from slides.

The Future is Digital

Mariano believes medical education must quickly incorporate digital pathology and AI training to prepare the next generation of pathologists. The Digital Pathology Association is key in fostering collaboration to expand access, especially in underserved communities.

While going digital requires some reinvention, Mariano is excited to pioneer this space. He and CorePlus aim to be "missionaries" doing what's best for patients and the field of pathology.

THIS EPISODE'S RESOURCES:

• Digital Pathology Association
• IBEX website
• Alpenglow Biosciences website
• Epredia website
• CorePlus website
• Amazon Link for the Digital Pathology 101 Book

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Swarm Learning in Digital Pathology: Revolutionizing Cancer Histopathology

Today on the Digital Pathology Podcast my guest is Oliver Saldana, the first author of a significant Nature Medicine paper published in 2022 on 'Swarm Learning for Decentralized Artificial Intelligence in Cancer Histopathology'.

Oliver shares his journey from Mangalore, India, to Germany, where he pursued his master's and PhD, delving into histopathology and decentralized AI under the supervision of Professor Dr. Jakob Nicolas Kather.

The discussion explores the concept of swarm learning as a novel method for deep learning in histopathology, its advantages over centralized learning including compliance with data protection laws like GDPR, and its potential for global collaboration in medical research without sharing sensitive data.

Oliver emphasizes swarm learning’s ease of setup and its alignment with the FAIR principles for scientific data management. The podcast aims to shed light on the groundbreaking work being done in the convergence of pathology and computer science, urging researchers and pathology centers to digitize their slides and contribute to global swarm learning projects.

00:00 Introduction to Swarm Learning and Its Applications
00:50 Intro
01:17 Meet Oliver Saldana: A Trailblazer in Decentralized AI for Cancer Histopathology
03:57 Exploring the Concept of Decentralized AI and Its Importance
06:52 Understanding Centralized vs. Decentralized Learning
08:47 The Revolutionary Approach of Swarm Learning
10:38 Blockchain's Role in Enhancing Histopathology with Swarm Learning
14:50 Addressing Preprocessing and Generalizability in Swarm Learning
21:26 Swarm Learning's Compliance with GDPR and Data Protection
25:05 Exploring Swarm Learning in Medical Data Analysis
25:34 Prototype Study and Real Cohorts in Swarm Learning
27:01 Comparing Swarm Learning with Centralized Models
27:44 The Role of Bare Metal Servers in Swarm Learning
30:01 Centralized Slide Repositories vs. Swarm Learning
44:11 Commercializing Swarm Learning Models
47:07 FAIR Principles and Swarm Learning
51:11 Global Ambitions and the Future of Swarm Learning

THIS EPISODES RESOURCES📝 Swarm Learning for decentralized and confidential clinical machine learning
🔗 https://www.nature.com/articles/s41586-021-03583-3

📝The FAIR Guiding Principles for scientific data management and stewardship
🔗https://www.nature.com/articles/sdata201618

🎧BIGPICTURE – THE LARGEST WHOLE SLIDE REPOSITORY FOR AI MODEL DEVELOPMENT IN PATHOLOGY. WHERE DO WE STAND AT MONTH 15/72?
🔗https://digitalpathologyplace.com/podcast/bigpicture-the-largest-whole-slide-repository-for-ai-model-development-in-pathology-where-do-we-stand-at-month-15-72/

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Navigating Ethical Challenges in AI-Powered Pathology

This episode is a webinar recording.
It delves into the complex ethical considerations of incorporating artificial intelligence (AI) in pathology.

Dr. Zuraw begins by exploring the fundamentals of ethics and moves on to discuss the impact of AI in pathology, focusing on:

• ethical dilemmas,
• data diversity issues,
• biases, and
• the importance of maintaining professional and societal ethical standards in the wake of AI integration.

The session touches upon the ethical guidelines and regulatory frameworks guiding ethical decision-making in healthcare, alongside the role of regulatory agencies like the FDA.

It also highlights the significance of data diversity and mitigation strategies to address potential ethical pitfalls in AI utilization.

The webinar emphasizes the constant balance between advancing technology and ethical responsibility, underlining the need for transparency, governance, and accountability in deploying AI tools in pathology.

00:00 Introduction to Ethics in AI-Powered Pathology
00:30 Exploring the Ethical Dilemmas in Healthcare
00:36 Webinar Overview and Digital Pathology Insights
01:05 Defining Ethics and Its Importance in AI Pathology
02:05 Interactive Webinar Engagement and Audience Participation
03:25 Deep Dive into Ethics: Definitions and Applications
09:31 Ethical Considerations in Biomedical Research
10:34 Navigating Ethical Dilemmas: A Practical Example
13:57 Understanding Ethical Principles in Decision Making
17:14 AI Bias and Representation in Pathology
20:52 Frameworks and Guidelines for Ethical Oversight
25:02 AI Applications in Pathology: Ethical Perspectives
27:21 Exploring AI in Research and Its Capabilities
29:22 AI's Role in Medical Imaging and Diagnostics
33:11 Ethical Considerations and AI in Pathology
34:04 Addressing AI Challenges: Bias, Interpretability, and Security
44:26 AI as a Medical Device: Regulatory Perspectives and Future Directions
49:16 Concluding Thoughts and Audience Engagement

THIS EPISODES RESOURCES:

• Interested in getting the slides from this presentation, click here
• AI in Pathology: What could possibly go wrong (Paper)
• Ethics of AI in Pathology: Current Paradigms and Emerging Issues (Paper)
• Guardrails for the use of generalist AI in cancer care (Nature commentary)
• No more microscopes. How close are we to glassless pathology? W/ Dr. Richard Levenson, US Davis Health (Podcast episode)

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If any of the statements applies:

➡️ You know AI and Machine Learning are already part of the pathology workflow, but maybe you are not exactly sure which part of the workflow?

➡️ “AI” is still a bit of overhyped, fuzzy buzzword for you?

➡️ You would like to learn about how it can help pathologist and labs work smarter and patients get better care.

Then this webinar is for you!

This is the second part of the “Digital Pathology 101” webinar series, based on the “Digital Pathology 101” book, where Dr. Aleks Zuraw explains digital pathology and AI concepts.

This journey through Chapter 3 illuminates how image analysis, AI, and machine learning not only complement traditional pathology but propel it into new realms of precision and insight.

As we delve into the essence of tissue image analysis and the transformative role of AI and machine learning in modern pathology, you'll discover how these technologies augment diagnostic methods, enhance research, and redefine what's possible in our field.

From the basics of tissue image analysis to the advanced realms of computer vision and the pivotal role of quality control, this webinar bridges the gap between high-level computational domains and daily pathology practice.

What You'll Explore:

The foundational principles of image analysis, AI, and machine learning in pathology.
The crucial balance between classical and AI-based approaches to tissue image analysis and their applications in both regulated and non-regulated environments.
The importance of quality control in ensuring accurate, reliable results from AI-assisted analyses.
An introduction to the key terminology of pathology informatics, demystifying the language that underpins digital pathology and AI.
Who Should Attend:
This webinar is tailored for:

🔴 pathologists,

🔴 researchers, and

🔴 healthcare professionals

who are eager to learn about and/or integrate AI and machine learning into their work.

Whether you're just starting or looking to deepen your expertise in digital pathology, this series offers invaluable insights into leveraging technology for enhanced diagnostic precision and patient care.

Date and Time:
April 11, 2024 at 9:00 - 10:30 a.m., EST

Location:
Online

AI is here, so let’s learn what it means for pathology

how can you leverage it for your work?

and how to navigate this new technology responsibly.

Looking forward to seeing you on the inside!

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What can digital pathology be used for? Is it just diagnostics or does it go beyond that?

In this webinar, based on chapter 4 of my “Digital Pathology 101” book

you will learn about:

• The transformative impact of digital pathology on healthcare.
• Strategies for integrating digital tools into clinical practice for improved outcomes.
• The pivotal role of digital pathology in advancing drug development and personalized medicine.

Insights and reflections on the future directions of our profession.

Join me for a session filled with enthusiasm, knowledge, and a shared vision for the future of pathology. Together, we'll uncover digital pathology's possibilities for our field and the broader healthcare community.

Your engagement and curiosity drive this field forward, and I can't wait to share this time with you.

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This week, the digital pathology community gathered at the United States and Canadian Academy of Pathology (USCAP) annual meeting in Baltimore. I had the incredible opportunity to attend, spurred by an invitation from Hamamatsu, known for their revolutionary digital pathology scanners like the FDA-cleared S360 and the new S20 model.

Key Takeaways from USCAP:

• Innovative Partnerships and Technology: My agenda was filled with meetings that explored the latest in digital pathology. Key highlights include the launch of Hamamatsu's S20 scanner, Techcyte's technological advancements, and Proscia's FDA-approved AP Dx software. The collaboration between Hamamatsu and Agilent, utilizing Proscia's platform, underscored the collaborative spirit driving the field forward.
• Community Engagement and Recognition: The warmth and recognition from the community were overwhelming. It reinforced the value of our work and the podcast, blending technical insights with personal stories that underline the human aspect of pathology.
• Advancing Research and Education: A standout moment was learning about the World Tumor Registry initiative from Andrey Bychkov and Alyaksandr Nikitski. This initiative marks a significant step in making valuable pathological data more accessible for research and education, starting with a comprehensive collection of thyroid case slides.

Looking Forward:The USCAP meeting was a testament to the enthusiasm and innovation within digital pathology. Stay tuned for a detailed video blog covering the conference, highlighting the S20 and more, coming soon on YouTube!

USCAP BULLET UPDATES

1. Hamamatsu: Known for their FDA-cleared scanner, S360, and the newly launched S20 model.
2. Techcyte: Discussed their dynamic evolution and upcoming projects and their partnership with BD for cervical cytology AI based evaluation.
3. Corista: Learned about their software advancements, including voice recognition for improving pathologist workflows.
4. Smart In Media: Discussed their new camera for microscopes and grossing rooms.
5. Proscia: Celebrated their FDA clearance for the AP Dx software.
6. Epredia: Showcased their pathology equipment, including scanners like the P1000 with water immersion.
7. Aiforia: Highlighted their AI for image analysis, especially the GLP-compliant module for toxicologic pathology.
8. Indica Labs: Featured their pathologist cockpit setup and discussed their comprehensive software solutions.
9. PathPresenter: Met with the CEO, Patrick Myles, and discussed the company's rapid growth and services.
10. Pramana: Discussed their archival scanning services and their new benchtop scanner that runs algorithms during scanning.
11. Grundium: Showed off their new four-slide scanning Ocus and the cute one-slide Ocus scanner.
12. Andrej Bychkov to discuss his poster on the use of ChatGPT by pathologists and told me about the World Tumor Registry initiative.

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She did it all on her own, to keep serving her patients.

In this episode of Digital Pathology Podcast, host Dr. Aleksandra Zuraw is joined by Dr. Elizabeth Plocharczyk, a pathologist based in Ithaca, New York.

Beth shares her experience integrating digital pathology into her practice at Guthrie Cortland Medical Center and Cayuga Medical Center at Ithaca, NY. Her journey offers insights into the practicalities of adopting digital tools in a community hospital setting.

🔥 The discussion highlights:

• The factors that influenced Beth to start using digital pathology.
• How digital tools have addressed challenges related to being a solo practitioner in a rural area.
• The significance of compliance, internal validation, and administrative support in transitioning to digital pathology.
• Suggestions for pathologists considering digital pathology, emphasizing iterative implementation and the importance of validation regardless of FDA approval status.

Dr. Plocharczyk's account underscores the role of digital pathology in enhancing the efficiency and flexibility of pathology practice, especially in geographically constrained settings.

The episode provides a REALISTIC OVERVIEW OF TRANSITIONING TO DIGITAL PATHOLOGY, including overcoming potential hurdles and leveraging technology for more effective pathology services.

Be sure to watch or listen to the full episode, as Dr. Plocharchyk reveals all the details about the equipment she used, the way she validated the system as well as her budget.

This episode is particularly relevant for pathologists and healthcare professionals exploring digital pathology's potential to improve practice management and patient care.

Questions that will be answered:

• Who is Dr. Beth? What did her pathology practice look like before digital pathology?
• When did Dr. Beth start using digital pathology?
• What was impossible before going digital?
• How did you determine which tools to use?
• Why did you opt for Whole Slide Imaging, for frozen sections?
• What was the budget and how did you know this is a reasonable budget?
• How long have you had the equipment?
• If budget was not a constraint, what would you add to your digital pathology arsenal?
• Are you building your tools or seeking the help of a third-party provider?
• What does the day-to-day practice with your digital pathology tools look like?
• How have the community hospitals responded to the new cutting-edge technology?
• What advice would you give to those starting with digital pathology?
• Would digital pathology help you grow your practice?

THIS EPISODE RESOURCES:

Smart in Media PathoZoom Live View and Scan:
🔗 https://www.youtube.com/watch?v=PZA9HX3qSfk&list=UULF-bagVf7bqp3L0bAdNYaaQg

Grundium Ocus Whole Slide Scanner:
🔗 https://www.youtube.com/watch?v=dq-vdOL9Q9Q&list=UULF-bagVf7bqp3L0bAdNYaaQg

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Remote Digital Second Opinions: Pioneering Global Patient Care

Imagine a future where accessing world-class diagnostic expertise is just a click away for any patient, anywhere. In this episode you will learn how remote digital second opinions, a specialized application of digital pathology, can drive global adoption of digital pathology and significantly expand access to patient care.

Together with Dr. Raj Singh, founder of PathPresenter, we explore this cutting-edge approach that promises to transcend current digital pathology uses, making specialized medical consultations more accessible and efficient than ever before.

The Evolution of Digital Pathology

Digital pathology is rapidly becoming indispensable in modern healthcare. It equips pathologists with advanced digital tools and platforms, significantly boosting the speed and scope of diagnoses. Remote second opinion has a transformative role here. By leveraging digital slides and cloud-based infrastructure, pathologists can collaborate seamlessly across distances, breaking down geographical barriers like never before.

PathPresenter: Filling the Gaps in Pathology Workflow

Dr. Singh unfolds the story behind PathPresenter, highlighting its inception, mission, and the significant void it fills within the pathology field. PathPresenter is more than a platform; it's a catalyst for bridging educational and clinical gaps in pathology. It enables effortless sharing and collaborative analysis of cases among pathologists globally, fostering a vast network of professional expertise.

Embracing the Digital Shift: A Call to Action

The shift towards digital workflows is not merely a technological leap but a comprehensive strategy to enhance patient care, ensuring diagnoses are faster, more accurate, and widely accessible.

The transition to digital pathology is inevitable and it is happening quickly. Dr. Singh emphasizes the urgency for pathologists and healthcare institutions to adapt to these technological advances proactively. Proactive adoption will give us the power to decide how we want to implement digital pathology and what tools we want to use. If the pathology community does not take charge of this process it will be imposed on us by others. We don't want to figure out how to digitize slides in a panic mode when other specialties require it for patient care. We want to be in the drivers seat and guide the patient care according to the most up-to-date pathology expertise.

Why This Matters More Than Ever

In an era where healthcare demands are ever-increasing, and the need for specialized knowledge is paramount, digital pathology and remote second opinions present an unprecedented opportunity. This application democratizes access to expert diagnostics, ensuring patients, regardless of location, receive the best care possible.

It's more than an advancement; it's a new way of thinking about and delivering pathology services. Explore the vast possibilities remote second opinions offer and how they serve as a bridge to a more connected, efficient, and patient-centric healthcare system.

THIS EPISODE'S RESOURCES

• PathPresenter website
• Pervious episode with Dr. Raj Singh (when PathPresenter was just for presentations:)

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Today our special guest is Dr. Keith Kaplan, the creator of TissuePathology.com himself! The publisher of a platform that inspired the creation of Digital Pathology Place.

The Digital Pathology Trailblazer on the Web

Dr. Keith Kaplan, a surgical pathologist and a pivotal figure in the digital pathology community, has significantly contributed to the field with his groundbreaking website, tissuepathology.com. His passion and dedication have made his platform the first resource many turn to when searching for anything related to digital pathology.

From Traditional to Digital

Dr. Kaplan's unique journey in pathology began in Chicago, shaped by his military service and academic path at Northwestern University. His early exposure to telepathology and digital imaging during his military tenure set the stage for his impactful venture into digital pathology, initiating a transformative career trajectory.

TissuePathology.com: A Pioneering Platform

Dr. Kaplan launched tissuepathology.com, driven by his enthusiasm for utilizing the internet to disseminate knowledge. This platform quickly became a leading blog in the digital pathology realm, motivating others to establish their blogs and engage in the dynamic digital pathology conversation.

The Evolution of Digital Pathology

Keith's work with robotic telepathology and his involvement in deploying digital pathology solutions across various settings highlight the significant advancements in the field. His stories of early digital pathology efforts, including the deployment of systems for military applications and the subsequent adoption in civilian medical practice, showcase the progressive integration of technology in pathology.

Embracing Change: The Digital Shift

Recently Dr. Kaplan's practice transitioned to digital pathology for primary diagnosis. The integration of digital pathology has streamlined diagnostic processes, enabling faster and more efficient patient care despite initial reservations about moving away from traditional microscopy.

Future Directions and Ongoing Challenges

Looking ahead, the future of digital pathology will be impacted by AI and the ongoing pathology workforce shortage. Keith emphasizes the need for the pathology community to adapt and embrace new technologies while also addressing regulatory, ethical, and practical challenges.

THIS EPISODE's RESOURCES:

• THIS EPISODE's RESOURCES:
  • Ten important lessons we have learned as pathology bloggers (Paper)
  • First 100 days of using digital pathology (LinkedIn post)
  • Digital pathology is just pathology, but does it matter anymore? (LinkedIn article)
  • Dr. Keith Kaplan's blog tissuepathology.com
  • Digital Pathology Club - course membership

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This is the audio version of the second episode of the DIGITAL PATHOLOGY NEWSLETTER. that should have already landed in your inbox if you are on my digital pathology trailblazer list.

(And if you are not, you can get on it here, and get a free PFD of my "Digital Pathology 101" book)

HERE ARE THIS EPISODE'S RESOURCES:

• Digital Pathology Trailblazer Corner
  • We hit 10K followers on LinkedIn! Thank you so much!
• Interesting DP Research & Developments
  • AI in Pathology: What could possibly go wrong (the official paper)
  • AI in Pathology: What could possibly go wrong (the authors version on LinkedIn)
• Interviews & Opinion from DP Experts
  • Last podcast episode with Dr. Richard Levenson about glassless pathology and AI. So informative and entertaining!
• Educational Resources
  • The FREE online course for those starting the digital pathology journey - The Digital Pathology Starter Kit
  • DPA Webinar "Remote Sign Out. Myths and Reality" by Dr. Giovanni Lujan
• Cool DP Equipment
  • Small microscope camera I'm exploring called PathoZoom
  • Podcast with Dr. Martin Weihrauch, the CEO of Smart in Media - the producer of PathoZoom
• Quick Bullet Updates of What I'm Up To
  • Waiting for the Foldscope, a little paper microscope, to arrive.

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Can pathology be truly digital without getting rid of glass?

In this episode with Dr. Richard Levenson, Professor and Vice Chair for Strategic Technologies at the Pathology Department of UC Davis, you’ll learn how close we are to “glassless pathology” and other digital innovations that could transform the field.

In this episode we cover:

• Richard's Background

With an eclectic background spanning English literature, medical school, research, and even a tech startup, Richard brings unique expertise in digital pathology. At UC Davis, he's pioneering new microscopy methods like MUSE and FIBI that enable imaging thick tissue sections without slides or stains.

• Pigeon Research

You may also know Richard for his famously viral research training pigeons to detect cancer in pathology slides. As he explains, “Pigeons have the skills to tell...tiny, tiny pattern differences” critical for pathological diagnosis. This project brought fun and creativity to his lab, even as they push new frontiers in glassless pathology.

• Histolix and Glassless Pathology

His company Histolix is commercializing the glassless pathology approach, which Richard envisions bringing pathology on par with radiology’s direct-to-digital workflow. Their validation study already achieved 97% concordance between glassless and standard H&E reads. As Richard explains, these techniques “open up the possibility for rapid intraoperative diagnosis without freezing or sectioning.”

• Digital Pathology Innovation

Combined with AI, innovations like these could automate workflow steps like staining, analysis, and prioritization. However, as their recent paper explores, AI does pose risks. Richard believes we must tread carefully, using human oversight and judgment to guide implementation. Still, he sees great potential to augment diagnostics with computational tools.

• Conclusion

There’s no better guide to exploring these frontiers than Richard. Tune into the full conversation using the link above for an insightful tour of digital pathology’s cutting edge. Check Histolix for the latest on their research, and access key publications from Richard’s lab through the links below. Where will you help take pathology next?

THIS EPISODES RESOURCES

• Histolix website
• A Pilot Validation Study Comparing Fluorescence-Imitating Brightfield Imaging, A Slide-Free Imaging Method, With Standard Formalin-Fixed, Paraffin-Embedded Hematoxylin-Eosin-Stained Tissue Section Histology for Primary Surgical Pathology Diagnosis
• Pocket MUSE: an affordable, versatile and high-performance fluorescence microscope using smartphone.
• AI in Pathology: What could possibly go wrong?
• Pigeons (Columba livia) as Trainable Observers of Pathology and Radiology Breast Cancer Images.

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This is the audio version of the first brand new DIGITAL PATHOLOGY NEWSLETTER. that should have already landed in your inbox if you are subscribed to my list.

If not you can join here (and get the PDF of my book for free!)

THIS EPISODE'S RESOURCES:

• Interesting DP Research & Developments
  • Swarm learning for decentralized artificial intelligence in cancer histopathology
• Interviews & Opinion from DP Experts
  • Last podcast episode with Dr. Talat Zehra about how AI is transforming pathology in developing countries
• Educational Resources
  • "Digital Pathology 101. All you need to know to start and continue your digital pathology journey"- Book
• Cool DP Equipment
  • Unboxing and Setup of a Microscope Monitor Display
• Quick Bullet Updates of What I'm Up To
  • Reading Digital Pathology Papers with Text to Speech Software | Natural Reader review

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What is the status of digital pathology in under-researched areas?

Is it even a thing? Can it be used? And in what capacity?

In this exciting episode with Dr. Talat Zehra, a trailblazing pathologist from Karachi, Pakistan, and a finalist on the Pathologist Power List we are answering all the above questions.

Dr. Zehra is a beacon of innovation and determination, reshaping the landscape of healthcare in her region.

🔍 Don’t Miss:

• The Trailblazing Journey in Digital Pathology: Learn about Dr. Zehra's path to becoming a leader in digital pathology.
• Addressing Challenges in Low-Resource Settings: Discover how digital pathology can overcome healthcare barriers.
• Impact of Digital Tools in Developing Nations: Explore the transformative effect of technology in pathology.
• The Future of Pathology: Insights into embracing AI and upcoming trends in the field.

Dr. Zehra takes us through her groundbreaking journey in digital pathology. She shares her evolution from using basic static imaging techniques to embracing AI-enhanced pathology, overcoming numerous challenges to pioneer advanced pathology technologies in Pakistan.

Listen as Dr. Zehra recounts her mission to elevate pathology education and technology. Her story is a powerful testament to how dedication and innovative thinking can break down global healthcare barriers, transforming the field of pathology with a blend of cutting-edge technology and unwavering perseverance.

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Exploring Image Analysis Innovation with Trevor McKee of Pathomics.io

If you work in digital pathology, you likely rely on image analysis tools to gain insights from complex visual data. But how do you stay on top of the latest innovations in this fast-evolving field?

In this podcast episode together with Trevor McKee, CEO of Pathomics.io, we discuss innovation in image analysis using open source tools.

Pathomics takes an innovative approach by building image analysis solutions on open source platforms like QuPath. As Trevor explained, open source fosters collaboration, democratizes access, and drives rapid advances - key in a fast-moving field like digital pathology. This enables rapid progress that proprietary systems can't match.

Trevor's Career Journey

Trevor’s journey lead him from chemical engineering into pioneering image analysis, inspired by solving complex biological problems. His diverse experiences, from photon imaging at MIT to leading a core lab facility, fueled a passion for leveraging image analysis to extract insights. Today, in addition to leading Pathomics.io he is an Adjunct Lecturer at the University of Toronto, and the Chief Scientific Officer at BioCache™ Lab Solutions.

Transparent and Reproducible Image Analysis & Explainable AI

A core ethos at Pathomics is making image analysis transparent and reproducible. through explainable AI techniques. Tools like XGBoost create models that are easier to interpret than "black-box" end-to-end neural networks. This builds trust and acceptance among the scientific community.

Streamlining Workflows

In addition, Pathomics develops solutions to streamline clients' image analysis workflows. For example, their Universal StarDist plugin makes it easy to run advanced models like StarDist in QuPath. Overall, the goal is to automate tedious tasks so you can concentrate on high-value decision making.

The Future of Image Analysis

Looking ahead, Trevor shared his vision for an AI-powered online platform enabling users to go seamlessly from images to insights. He also discussed open wikis to prevent redundant work and encourage knowledge sharing as the field rapidly evolves.

Trevor plans to launch it to catalogue digital pathology resources such as image analysis focused machine learning papers to prevent redundant research work and encourage knowledge sharing as the field rapidly evolves.. It aligns with his commitment to open science and community knowledge sharing.

Key Takeaways

I came away from our wide-ranging discussion with an insider’s view of the huge potential of image analysis to transform digital pathology. By leveraging open source tools and staying atop the latest advances, you can work smarter and unlock new capabilities.

So tune in to explore these innovations and more from a leader in the field! The episode provides practical insights you can apply to make the most of the newest techniques

THIS EPISODE'S RESOURCES:

• Pathomics.io website
• Pathomics Wiki with tissue image analysis papers
• Interested in contributing to Pathomics Wiki? Submit your entry here.
• List of open source software for image analysis
• Digital Pathology Club Free Tr

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In this episode, based on a webinar I recently gave, I delve deep into the captivating world of Natural Language Processing (NLP) and its role in pathology.

Have you ever pondered how language models like ChatGPT are shaping our scientific understanding?

Or how they might redefine the way we process and interpret vast amounts of data?

Let's embark on this journey together as I share my insights and findings.

Key Points:

• The ChatGPT Debate: Is ChatGPT a revolutionary tool or a looming data concern? I've heard the buzz in the digital pathology community, and I'm here to shed light on this debate.
• Natural Language Processing (NLP): As a branch of AI, NLP is transforming industries. From tools like Google Translate to Siri, it's evident NLP's influence is vast. But how does it intertwine with pathology?
• The Might of Large Language Models: Imagine models trained on data equivalent to streaming 4K movies non-stop for years! Their ability to predict and generate text opens up a world of possibilities.
• The Transformer Architecture: It's the game-changer in NLP. It's not just about words; it's about discerning patterns and extracting logic from data.
• Ethical Considerations: With the power of these models comes immense responsibility. I'll discuss the ethical dilemmas we face, especially in medicine and pathology.

The horizon of pathology is expanding with the advancements in AI and NLP. As I delve deeper into tools like ChatGPT, I believe it's imperative to stay updated and make informed decisions.

I prepared a book for you that is a great starting point: "Digital Pathology 101".
You can grab the FREE PDF here.

Interested in viewing the webinar presentations itself? You can view the webinar here.

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The Future Landscape of Digital Pathology: Insights from Kate Lillard Tunstall, Indica Labs

What insights can be gained from a 12-year-long digital pathology journey as part of one of the leading tissue image analysis solution providers? A lot has happened in that time and Kate Lillard Tunstall, the Chief Scientific Officer at Indica Labs, shares her vast knowledge and experiences in this podcast episode. With a career spanning over a decade, Kate has witnessed firsthand the transformative shifts in the industry.

The Genesis of Halo:

Kate reminisced about the early days of Indica Labs and the birth of their core product, the Halo platform. Designed with precision and adaptability in mind, Halo has become a beacon in the digital pathology and tissue image analysis landscape. The platform's name, inspired by the unique halo-like appearance around cells visible during image analysis, showcases Indica Labs' attention to detail and their connection to the core of pathology.

Services Beyond Software:

Indica Labs isn't just about software; they offer a plethora of services tailored to the needs of the pharma sector and beyond. Their pharma services team, which has been around the longest, acts as a bridge between product development and real-world application. By serving as an internal customer, this team ensures that Indica Labs' offerings are not only cutting-edge but also practical and user-friendly.

Embracing the AI Revolution:

The integration of AI into digital pathology was a significant pivot point for Indica Labs. Kate candidly shared her initial skepticism towards AI's role in pathology. However, witnessing the profound impact of deep learning, especially in tissue classification, turned her into a believer. By 2017, Indica Labs had fully embraced AI, setting itself apart in the industry.

Looking Ahead:

Kate's vision for the future is a world where digital pathology isn't the exception but the norm. As more hospitals and health systems go digital, the volume of data will skyrocket. This data surge, combined with the power of AI, promises unprecedented advancements in pathology. Kate also shared lessons from Strata, a project aimed at merging image analysis data with patient data. Spoiler alert - the project was not pursued, but the challenges it faced underscored the importance of innovation adaptability and a deep understanding of customer needs in the world of digital pathology.

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DigitalPathology #PathologyInnovations #IndicaLabs #AIRevolution #Halo #FutureTrends

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Have you started your digital pathology journey already?

Chances are that if you are reading this, you have. You have started it in a particular point of "digital pathology entry". Maybe it was tissue image analysis, virtual rounds on whole slide images or validation of a scanner.

My "digital pathology entry point" was tissue image analysis and only through the lens of this application have I learned what are the other digital pathology applications.

In this chapter you will learn about all the current applications of digital pathology.

Because of where I started my journey I will always be biased towards tissue image analysis and AI, but revisiting the overview provided in this chapter will help me have all the other applications in mind, when I continue my journey of promoting digital pathology in the scientific and medical community.

I hope it will be a good basis for you as well. So let's dive into the contents.

Here is what you will learn in Chapter 4 of the "Digital Pathology 101" book:

We'll start by looking at the clinical applications. This includes

• the use of digital pathology for primary diagnosis in surgical pathology and cytopathology.

It facilitates more detailed examination and collaboration between pathologists. We'll also discuss

• how telepathology enables remote intraoperative consultations and second opinion consults.

And we'll touch on the

• education and training benefits, from resident teaching to continuing medical education.

Moving to research, we outline key applications like

• quantitative image analysis,
• AI and machine learning for predictive modeling, and
• high throughput analysis.
• collaboration, allowing researchers to simultaneously access images
• large scale studies and validation across institutions.

In drug development, digital pathology enhances

• preclinical histopathology and
• biomarker evaluation
• clinical trials by eliminating slide shipment and enabling centralized review.

Digital tools can also assist in developing companion diagnostics, although regulatory requirements here are still evolving.

While each application has its challenges, the overarching benefit of digital pathology is its

• capacity to connect workflows,
• enhance efficiency, and
• open new possibilities across clinical, research, and drug development spheres.

Understanding the breadth of these applications provides a compass for navigating our own digital pathology journeys.

Enjoy this chapter and I'll talk to you in chapter 5.

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Toxicologic pathology plays a critical role in drug development, yet its intersection with digital pathology is often overlooked. As a veterinary pathologist, I want to shed light on this important application.

This is Chapter 5 of the "Digital Pathology 101" book and in this chapter, you will learn how whole slide imaging is transforming preclinical trials. I'll explain key concepts like creating faithful digital replicas of glass slides. We'll also dive into validations needs for digital systems in regulated GLP studies.

Whole Slide Imaging Overview

I'll start by explaining whole slide imaging. This technology creates 2D digital copies of glass slides. The focus is not 3D images, but flat digital images containing the visual information pathologists need for analysis and reporting.

The FDA states these digital images can substitute for glass slides in preclinical toxicity studies, provided they meet requirements as "faithful digital replicas." With proper validations, digital slides enable remote assessments for multisite trials.

Validation and Documentation

For regulated GLP studies, replacing glass slides necessitates validating the whole digital pathology system. This includes IT infrastructure, scanners, software and more based on intended use.

Documentation is also key. Peer review statements should note the use of digital slides. Images must be securely stored and transmitted to maintain raw data integrity.

Conclusion

In closing, the FDA's guidance on digital pathology in preclinical trials signals an important step towards regulatory acceptance. Digital tools promise more controlled, efficient toxicity assessments, ultimately advancing drug development.

This chapter provides a compass for teams navigating digital pathology in regulated environments. Understanding principles of validation, security, and transparency allows us to realize the benefits while ensuring high standards.

You can find the original FDA guidance document this chapter is based on here:

• Use of Whole Slide Imaging in Nonclinical toxicology Studies: Questions & Answers.

Or you can watch me explain the guidelines here:

• What does the FDA Say About Digital Pathology for Nonclinical Toxicology Studies?

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Image analysis has supported pathology since the introduction of whole slide scanners to the market, and when deep learning entered the scene of computer vision tissue image analysis gained superpowers.

There are regulatory compliant AI-based image analysis tools available for practicing pathology around the globe.

So what shall you do, just embrace them and start using?

I would learn a bit about image analysis and AI first, to be able to make an informed decision.

Good news, you can get all the information needed for this informed decision from this very chapter of the "Digital Pathology 101" book that I have published for you.

From Chapter 3 you will learn the fundamentals of tissue image analysis and how it helps extract meaningful data from digital pathology images.

We break it down into basic concepts like

• regions and objects of interest,
• matching computer vision techniques to pathology tasks, and the
• differences between classical machine learning and AI-based deep learning approaches.

Understanding these foundations sets the stage for appreciating how image analysis is applied in regulated clinical settings versus exploratory research environments. You will learn the importance of quality control, because flawed data inputs inevitably lead to faulty outputs, regardless of the analysis method used.

Moving on, you will familiarize yourself with the key terminology from the world of artificial intelligence and machine learning.

The chapter clarifies the meaning of concepts like

• supervised learning,
• GPUs,
• data augmentation, and
• heat maps.

It emphasizes how techniques like

• patching and
• data augmentation

enable the training of machine learning algorithms on large datasets.

Ultimately, by comprehending this terminology and the basics of tissue image analysis, you'll gain clarity on how these tools can provide decision support to pathologists through computer-aided diagnosis. Rather than seeing AI as a black box, you'll have insight into how it arrives at its outputs.

With this balanced understanding, you'll be equipped to make discerning choices about embracing AI tools in your pathology practice, leveraging their benefits while being aware of current limitations.

Stay tuned as we continue unpacking the transformative potential of digital pathology!
Talk to you in chapter 4!

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As enthusiastic as the digital pathology community is about digital pathology, you are also grounded in reality and know that like every technology, digital pathology in parallel with its enormous benefits also has some drawbacks.

This is the second chapter of the "Digital Pathology 101" book and in this episode, I take a balanced look at the pros and cons of going digital.

Benefits

First, we highlight some of the key advantages:

• enhanced accuracy and efficiency in diagnostics,
• seamless collaboration opportunities,
• advanced research capabilities,
• integration with digital health systems, and
• exciting educational prospects.

Real-world examples showcase how these benefits have been leveraged, like the successful implementation of digital workflows in a large US hospital and the application of digital pathology in pharmaceutical research.

Challenges

However, we acknowledge this new frontier has its challenges. Technological hurdles around

• image quality,
• data storage, and management are significant.
• Navigating regulatory compliance and
• acceptance within the pathology community will take time.
• Cost-efficiency and specialized training remain issues to tackle.

Yet for each obstacle, there are solutions and opportunities to learn. Case studies teach us how institutions overcame cost barriers through long-term planning and addressed training needs via technology partnerships.

Constant advances promise more efficient scanning and sophisticated cloud storage on the horizon. And an evolving regulatory environment is steadily validating digital tools, albeit with a need to standardize guidelines.

While adoption is uneven, momentum is building towards digitization. By understanding the landscape and staying engaged with developments, pathologists can shape an ethical integration of these tools. Guided by both optimism and pragmatism, we can realize the potential of digital pathology to transform patient care.

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This is the second part of the first chapter of the recently published “Digital Pathology 101” book.

This part of the chapter addresses a question that I keep hearing from those just entering the world of digital pathology: “Will pathologists lose their jobs now, that algorithms can be developed to diagnose disease?”

The short answer is “No”.

Keep reading for the explanation why not.

The Rise of Deep Learning

One of the most notable trends has been the rise of deep learning and AI in digital pathology. These advanced techniques are being embraced by the pathology community to analyze complex issues from sclerotic glomeruli through liver fibrosis to different types of cancer. The user-friendliness of new tools powered by deep learning makes it accessible even for non-experts.

Industry Paradigm Shifts

Several paradigm shifts are occurring in the digital pathology industry:

• Transition from handcrafted algorithms to deep learning
• Shift to cloud-based Software as a Service (SaaS) solutions
• Movement towards pathologist decision support systems rather than fully autonomous analysis
• Enhanced user-friendliness of digital pathology software

Empowering Pathologists

An important change has been the emphasis on empowering pathologists with decision support systems rather than replacing them with algorithms. The goal is to accelerate the case review process without compromising accuracy or integrity. Pathologists remain responsible for the final diagnosis.

Blending Analog and Digital Worlds

Some innovative companies are pioneering solutions to blend traditional microscopes and digital pathology, such as Augmentics' augmented reality microscope cameras or systems used by Smart in Media. This allows professionals to collaborate in real-time and apply algorithms while still using the cherished microscope.

Personalized Digital Pathology

The industry has moved away from a one-size-fits-all approach to personalized solutions tailored to each institution's workflow and challenges. This shift leverages the power of deep learning while enhancing user experience.

The trusted microscope remains an essential part of pathology, but digital solutions open new doors for analysis and efficiency. As this field evolves, quality control and understanding the capabilities and limitations of technology is crucial.

Exciting times are ahead in digital pathology! Be sure to listen to the full podcast episode for an in-depth discussion.

Get the PDF of "Digital Pathology 101" Book here

Get the paper copy of "Digital Pathology 101" on AMAZON

Read the original blog post "New Trends and Paradigm Shifts in the Digital Pathology Industry"

Watch the "Digital Pathology 101" Book Launch here

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I'm thrilled to introduce you to a long-awaited companion in your digital pathology voyage – the book, "Digital Pathology 101 - All you need to know to start and continue your digital pathology journey."

This book is the culmination of months of passion and hard work. If you've been following me on social media, you know it's been a labor of love. But why did I write this book, you might ask? Well, it's your comprehensive guide to navigating and thriving in the realm of digital pathology.

But first, let's rewind a bit. Back in 2003, Dr. Anil Parwani predicted that everyone would be digital by 2007. Well, that might have been a bit too optimistic, but guess what? The digital age in pathology is here, and it's not a distant future; it's right around the corner.

I'm convinced that now is the time, and that's why I'm so excited to share this book with you.

If you missed our webinar launch, don't worry – you can catch the replay here .

In that webinar, I delved deep into why digital pathology is the future, and trust me, it's a future you don't want to miss out on.

But enough about that, let's dive into the first chapter of the audio version of "Digital Pathology 101." In this chapter, we'll explore the historical milestones that paved the way for digital pathology. So, without further ado, let's get started on this journey into the world of digital pathology.

Here is what we will cover in this part of chapter 1:

DIGITAL PATHOLOGY MILESTONES

• A. Historical Milestone
• B. Regulatory Milestone

BASIC DIGITALIZATION CONCEPTS

• A. About Digitization, Digitalization and Digital Transformation
• B. Digitization - The Scanner and its Components
• C. Digitalization and its challenges - Data Generation and Management
• D. Digital transformation: Advantages and Challenges of Digital Pathology

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In this episode of "The Digital Pathology Podcast," we delve into the fascinating career of Dr. Anil Parwani from Ohio State University, a visionary whose ardor for technology and research paved the way for groundbreaking advancements in digital pathology.

Dr. Parwani's journey commenced with a bold move – launching a web educational series during his residency – well ahead of digital pathology's mainstream emergence. As we delve into his narrative, you'll witness how his pioneering spirit laid the groundwork for a transformative trajectory. The pivotal moment? It arrived with the debut of the first digital pathology scanners. Dr. Parwani envisioned a future where patient care and pathology research could soar to unprecedented heights through digitization. His role in implementing digital pathology solutions, including collaborations with startups, deepened his grasp of the clinical significance of this game-changing technology.

As the COVID-19 pandemic accelerated technological advancements in digital pathology, Dr. Parwani witnessed a significant 20% surge in adoption within his institution. How did they strike the ideal balance between remote and in-person interactions? Discover the insights in this episode.

Furthermore, in an era where the number of medical students pursuing pathology is dwindling, we'll examine how digital pathology is sparking renewed interest. Dr. Parwani reveals how this field, with its research prospects, educational promise, and collaborative ethos, is reshaping perceptions and attracting fresh talent.

Stay tuned for an expedition through the dynamic realm of digital pathology with Dr. Anil Parwani. It's a captivating odyssey into innovation, precision, and the future of medical science that promises not to disappoint!

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EPISODES YOU WILL ALSO ENJOY:

• The Best Online Pathology Book Ever w/ Nat Pernick, PathologyOutlines.com
• What the Heck is DICOM in Pathology w/ David Clunie, PixelMed Publishing

DIGITAL PATHOLOGY RESOURCES:

• Digital Pathology Club Membership
• Bridging the Gap between Pathology and Computer Science - FREE Online Course
• Digital Pathology Starter Kit

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What happened to digital pathology in the last decade?

Step into a time machine with us as we explore "The Evolution OF Digital Pathology– From Improved Histology Quality to Fair Use of Pathology Data" alongside Dr. Matt Leavitt, President of the Digital Diagnostics Foundation and Founder of Lumea. In this captivating podcast episode, we'll journey through the years and witness the incredible transformation of digital pathology.

Travel back to 2013, when digital pathology was still in its infancy, and fast forward to the present day, where innovation and technology have reshaped the landscape and ethical questions about patient data use urgently need answers.

Dr. Leavitt provides unique insights into the challenges, breakthroughs, and trends that have defined this transformative decade.

Gain a front-row seat to the evolution of healthcare innovation as we compare and contrast digital pathology then and now. Whether you're a seasoned pathologist, a tech enthusiast, or simply curious about the future of medicine, this episode promises to enlighten and inspire.

Join us on this remarkable journey through time and innovation. Subscribe to the podcast now to uncover the secrets of digital pathology's evolution and chart a course for the future. Don't miss out—tune in and be a part of this fascinating exploration!

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EPISODES YOU WILL ALSO ENJOY:

• BigPicture The Largest Whole Slide Repository for AI Model Development in Pathology.
• Digital Pathology for Dermatologists. How Pathology Watch managed to Incorporate Digital Pathology in dermatology Practices across the US.

DIGITAL PATHOLOGY RESOURCES:

• Digital Pathology Club Membership
• Bridging the Gap between Pathology and Computer Science - FREE Online Course
• Digital Pathology Starter Kit

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How is digital pathology used in clinical trials? Because digital pathology as a discipline began with the aim of streamlining clinical trials, one could assume that this is currently the default.
Unfortunately, this is not the case… In today's discussion, our guest, Dr. Monika Lamba, a pathologist from Q2 Solutions, the lab division of IQVIA, sheds light on how digital pathology revolutionizes the landscape of clinical trials but also where we can still see the gaps.

In this engaging conversation, we discover how the origins of telepathology marked the inception of digital pathology and its journey to becoming an essential component of clinical trials.
Dr. Lamba walks us through the complexities of clinical trials, their organization, and patient matching across multiple sites and international boundaries.

As we unravel the role of pathology in clinical trials, we delve into how eligibility criteria, participant engagement, and informed consent are intricately woven into the process. Dr. Lamba educates us on the critical role of pathology in stratifying and randomizing patients, as well as evaluating outcome measures.

From disease staging to pathologic complete response assessments, pathology guides the way toward precision medicine and targeted therapies. Don't miss this captivating episode where we explore the synergy between digital pathology and clinical trials, paving the path for medical advancements and transformative healthcare solutions. Tune in now to expand your horizons on the ever-evolving intersection of digital pathology and clinical trials.

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EPISODES YOU WILL ALSO ENJOY:

• Is this the year of AI in Pathology? And what about ChatGPT? A crossover podcast with "Beyond the Scope"
• What you need to know about Digital Pathology Trends: Takeaways from the DP&AI Global Engage Event w/ Giovanni Lujan, Ohio State University

DIGITAL PATHOLOGY RESOURCES:

• Digital Pathology Club Membership
• Bridging the Gap between Pathology and Computer Science - FREE Online Course
• Digital Pathology Starter Kit

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Welcome to a very spontaneous and exciting episode of the Digital Pathology Podcast. In this episode, I had the pleasure of sitting down with Dr. Giovanni Lujan from Ohio State University, whom you might remember from our previous crossover podcasts with Beyond the Scope.

Recently, we were at the Digital Pathology and AI Congress in New York organized by Global Engage, and guess what? We decided to record this episode right there, surrounded by the buzz of the conference. No fancy preparations, just real and raw insights for you.

Giovanni and I are sharing our impressions and discussing the latest trends in digital pathology that were highlighted at the Congress. It's fantastic to finally meet in person after collaborating on two podcasts together. Giovanni has been a devoted follower of our podcast and all things digital pathology, and I'm truly inspired by his passion for the field.

The Congress organized by Global Engage has a unique vibe. It's smaller, which allows for more meaningful interactions and networking opportunities with fellow professionals and vendors. The longer breaks and one-on-one meetings foster valuable connections, making this conference stand out from the rest.

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THIS EPISODE'S RESOURCES:

• What's up in Digital Pathology in 2022? (digitalpathologyplace.com)
• AI in Pathology: Unveiling the ChatGPT Connection in Pathology (digitalpathologyplace.com)
• Giovanni Lujan MD in LinkedIn

DIGITAL PATHOLOGY RESOURCES:

• Digital Pathology Club Membership
• Bridging the Gap between Pathology and Computer Science - FREE Online Course
• Digital Pathology Starter Kit

Keywords: Digital Pathology Congress Recap, Networking, Insights, Global Engage Impact, Giovanni Lujan, Beyond the Scope, Cutting-edge Innovations, Stay Updated, Join Now

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Bringing Science into the Clinic with Prof. Anant MadabhushiTranslational research - what is it actually? How do you do it?

I can already tell you how not to do it - halfheartedly.

If you want to translate your scientific discoveries into something that actually benefits patients, you need to do all in!

And this is what my guest Prof. Anant Madabhushi from the Emory University and Georgia Tech has dedicated his entire professional career to.

He offers his insights on what it really takes to "walk your scientific talk" and work as a truly translational researcher in the space of digital pathology, radiology and medical engineering.

Listen to an in-depth discussion about conducting high-quality science and the rigorous journey of commercializing the research and actually benefiting the patients with it.

With his vast experience and profound understanding, Prof. Madabhushi gives us an insider's view of the effort and time required to successfully take a scientific discovery from the lab to a clinical trial, and then to the market. His perspective is enriched by his role as founder of several med tech companies, co-author of numerous high impact factor scientific publications, and a mentor and teacher to the next generation of brilliant computational pathology scientists.

THIS EPISODE'S RESOURCES:

• Anant Madabhushi on LinkedIn
• 🎥 Video from Anant's email signature
• 🎙️ Podcast with Rish Pai
• 📃 Paper "Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer"
• 📃 Paper "Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma"
• ✅ Anant's company Picture Heath

DIGITAL PATHOLOGY RESOURCES:

• Bridging the Gap between Pathology and Computer Science - FREE Online Course
• Digital Pathology Starter Kit

Keywords: digital pathology, translational research, image biomarkers, clinical practice, healthcare professionals

Support the showGet your "Digital Pathology Beginners Guide" E-book for free! Sign up for the waiting list here.

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Welcome, everyone, to the podcast! Today, I have the pleasure of introducing our guest, Lorenz Rognoni, the Director of Image Data Science at Ultivue. Ultivue specializes in spatial biology and image analysis, and Lorenz is an expert in the field of multiplex immunofluorescence (IF) and image data science. In our conversation, we dive into the challenges posed by multiplex IF when it comes to image analysis.

We discuss the challenges encountered in multiplex IF analysis, such as tissue preparation artifacts, tissue morphology, and antibody-specific staining. Lorenz emphasizes the heterogeneity of tissues, which can vary across different parts of the body, species, and indications, making automated analysis difficult. While visual evaluation by experts works well for classical stains, high-dimensional data requires a different approach.

Regarding the role of brightfield imaging in spatial biology, Lorenz explains that it still has its place, especially for robust and scalable analysis. While multiplex IF provides maximum information during the exploratory phase, he suggests transitioning to simpler approaches, such as singleplex IF or even brightfield imaging, when the focus shifts to specific biomarkers and data mining.

Finally, we discuss the potential pitfalls and challenges in transitioning from image analysis to analyzing the data generated. Lorenz emphasizes the need to extract meaningful information from millions of cells, define relevant phenotypes, and consider the downstream data mining process.

THIS EPISODE'S RESOURCES:

• Lorenz Rognoni on LinkedIn
• Ultivue Official Website

• Bridging the Gap between Pathology and Computer Science - FREE Online Course
• Digital Pathology Starter Kit

Support the showGet your "Digital Pathology Beginners Guide" E-book for free! Sign up for the waiting list here.

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Welcome to the crossover podcast with David, Giovanni and myself (Aleks) again. During this episode, we explore the world of digital pathology, artificial intelligence, including Chat GPT, and their growing importance in the field.

Is 2023 the year of AI for digital pathology?

We will talk about it and about the impact of AI in digital pathology and how Chat GPT could transform the way pathology reports are written. We discuss the benefits of using AI in digital pathology and what the future holds for this field.

As the discussion progresses, the experts explain the workflow of digital pathology and its advancements, including deep learning, and the role of AI in these advancements. They also discuss how Open AI Chat GPT is changing the landscape of artificial intelligence news.

Join Giovanni, David and myself for an engaging and insightful conversation about the latest advancements in digital pathology and the future possibilities of AI and Chat GPT in this field.

THIS EPISODE'S RESOURCES:

• What's up in digital pathology? - our first crossover podcast with David and Giovanni
• Beyond the Scope podcast with David and Gionanni

Support the showGet your "Digital Pathology Beginners Guide" E-book for free! Sign up for the waiting list here.

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Welcome to the podcast, my fellow digital pathology enthusiasts. Today, we have a special guest who defies the stereotype of a pathologist hidden behind the microscope. Dr. Marilyn Bui, a specialized cytopathologist, is patient-focused and emphasizes the patient-centricity of pathology work. She co-authored a book, the Healing Art of Pathology, and amplifies her message by being a leader in various organizations. Dr. Bui is the current president of the Florida Society of Pathologists and previously held the same role in the Digital Pathology Association.

In this episode, Dr. Bui shares her background and how she became a patient-centered pathologist. She talks about her work in tissue pathology, cytopathology, and digital pathology at Moffitt Cancer Center in Tampa, Florida, where she also teaches and conducts research. Dr. Bui believes that pathology and laboratory medicine are essential disciplines in healthcare, and she advocates for their protection and augmentation.

Join me in this conversation with Dr. Marilyn Bui as we delve deeper into the world of pathology and learn more about her book, the Healing Art of Pathology.

THIS EPISODE'S RESOURCES:

• The Healing Art of Pathology by Dr. Marilyn M. Bui
• Dr. Marilyn M. Bui
• Bridging the Gap between Pathology and Computer Science - FREE Online Course
• Digital Pathology Starter Kit

Get your "Digital Pathology Beginners Guide" E-book for free! Sign up for the waiting list here.

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Introduction
Are you curious about what goes into creating a cutting-edge digital online resource like PathologyOutlines.com? Then this episode is for you!

About PathologyOutlines.com
PathologyOutlines.com is a living textbook that covers 4,800 topics and involves 300+ contributors and 60 editors. It's a comprehensive online pathology resource that provides invaluable information for anyone in the pathology space.

PathologyOutlines.com Peer Review Process
The PathologyOutlines.com team takes great pride in their accuracy and responsiveness, as evidenced by their peer review process and willingness to address typos and other errors brought to their attention by users immediately.

Contributing to PathologyOutlines.com
PathologyOutlines.com is seeking contributors who are willing to submit their own images and articles to the website. This is a fantastic opportunity for anyone in the pathology field who is looking to expand their online portfolio and make a valuable contribution to the industry.

Personal Profile on PathologyOutlines.com
PathologyOutlines.com offers the chance to create a mini personal page on their website. This is a great opportunity for anyone practicing pathology in the world to be featured in the PATHOLOGIST DIRECTORY.

IHC Stains and CD Markers Explained
The page with all the IHC stains and CD markers explained is a favorite resource of many pathology professionals. This is an invaluable resource for anyone working in the IHC quantification space.

Digital Pathology Starter Kit
For those just starting their journey in digital pathology I have a special gift - the Digital Pathology Starter Kit. It contains valuable resources and information to help you get started on your digital pathology journey. This includes tips on how to choose a scanner, recommendations for digital pathology software, and much more.

Keywords: digital pathology, pathology professionals, PathologyOutlines.com, online pathology resource, peer review process, contributors, IHC stains, CD markers, digital pathology starter kit, personal profile.

THIS EPISODES RESOURCES:

• PathologyOutlines.com
• PathologyOutlines YouTube channel
• Pathologist Directory
• Immunohistochemistry stains and CD markers
• Digital Pathology Starter Kit

Get your "Digital Pathology Beginners Guide" E-book for free! Sign up for the waiting list here.

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THIS EPISODE'S RESOURCES:

• TriMetis Life Sciences Website
• Bridging the Gap between Pathology and Computer Science - FREE Online Course
• Digital Pathology Starter Kit

And if we are not connected already, let's connect on LinkedIn!

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The field of pathology has been revolutionized by the introduction of machine learning techniques, which enable more efficient and accurate diagnoses and have the potential to some day even eliminate or reduce the number of expensive molecular tests. However, the model development is a complex process and there are certain mistakes that must be avoided when using machine learning for pathology.

In this informative discussion with Heather Couture, an expert in machine learning for pathology, she highlights the top 5 MISTAKES THAT YOU MUSTAVOID to ensure the best possible machine learning and deep learning project outcomes.

Through her insights, you will learn about the 5 most common ML mistakes and how to avoid them:

1. Not understanding your data and its challenges.
2. Diving in without researching prior work (academic research and open source code) that is similar to what you're trying to model.
3. Starting with too complex a model.
4. Not thinking ahead towards validation.
5. Not fully understanding how the technology will ultimately be used.

By avoiding these common mistakes, you can maximize the benefits of machine learning for pathology and ensure accurate and timely project results and product launches. Whether you are new to machine learning or an experienced practitioner, this discussion is a valuable resource for anyone interested in using machine learning (including deep learning) for pathology.

THIS EPISODE'S RESOURCES:

📰 Heather's amazing newsletter (Computer Vision Insights)
🎧 Heather's fantastic podcast "Impact AI"
🎙️ Aleks' previous podcast with Heather (1) - Why machine learning expertise is needed for digital pathology projects
🎙️ Aleks' previous podcast with Heather - How to make machine learning models more robust

And if we are not connected already, let's connect on LinkedIn!

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Today's podcast is about the regulatory aspect of digital pathology and how it fits into the space between research and clinical use called translational medicine.

The podcast guest, Esther Abels, is a regulatory expert in digital pathology and a female leader in the field. She was involved in the team effort that brought the first Phillips clearance of a whole slide scanner to the attention of the FDA.

Translational research has the potential to bridge the gap between discovery and clinical practice. Its goal is to use evidence from research to target diseases and apply the insights in the clinic.

Digital pathology is seen as a tool to expedite the development pipeline for drugs and medical devices through the use of algorithms and AI.

There are however regulatory requirements that need to be taken into consideration when developing and using digital pathology tools. For example tissue image analysis tools used to support clinical decisions need to adhere to the FDA's guidance for software as a medical device.

The FDA is also working to define data sets that can be validated and reused for algorithm development.
There are ongoing efforts in Europe and the US to draft laws and frameworks related to artificial intelligence and validation techniques for AI tools.

It is a best practice to engage with the FDA early and this process for drug and medical device companies starts with a pre-submission to the FDA, seeking advice and discussing the approach. To be successful the role of a regulatory architect is crucial in overseeing the process and guiding it from point A to B to Z.

In addition to being a regulatory expert in the digital pathology field, Esther is also the immediate past president of the Digital Pathology Association (DPA). Because digital pathology brings people together from various fields, including pathologists, toxicologists, lab personnel, regulatory experts, and clinical development personnel, during her presidency Esther focused on collaboration between those different fields.

Esther Abels is a regulatory consultant who can be found on LinkedIn and her YouTube channel, which features helpful guidance and information videos.

THIS EPISODE'S RESOURCES:

✔️ Previous podcast with Esther: REIMBURSEMENT FOR DIGITAL PATHOLOGY IN THE CLINIC – HOW DOES THAT WORK? W/ ESTHER ABELS, VISIOPHARM
✔️ FDA GUIDANCE - CLINICAL DECISION SUPPORT SOFTWARE
✔️ FDA GUIDANCE - SOWTWARE AS A MEDICAL DEVICE
✔️ FDA GUIDANCE LIST FOR DIGITAL HEALTH
✔️ Beyond the Scope Podcast "CPT Coding and Digital Pathology Reimbursement"
✔️ ESTHER ABELS LINKEDIN
✔️ ESTHER ABELS YOUTUBE

💻 Bridging the Gap Between Pathology and Computer Science

And if we are not connected already, let's connect on LinkedIn!

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Today is the International Women's Day and this month at the Digital Pathology Podcast I decided to invite some incredible women who are leaders in the digital pathology field.

Today's guest, Inti Zlobec is a professor of Digital Pathology at the University of Bern. Inti is now leading the digital pathology branch of the Institute for Tissue Medicine and Pathology, where she bridges the gap between pathologists, computer scientists, and data scientists. She also serves as the president of the Swiss Digital Pathology Consortium. The institute's name was changed to emphasize the dynamism in pathology and its links to various other domains.

Her background is in statistics and computational research combined with a PhD in experimental pathology at the University of McGill in Canada. Combining and hybridizing those two fieldshas been a blessing for her in bringing people with different backgrounds together. Bothe her background and personality make here a natural connector of all digital pathology links.

A crucial part of this linkage is removal of the intimidation factor associated with pathologists. Instead the focus should be on acquiring the necessary level of knowledge for collaboration. It's important to involve pathologists in the projects early and give them them a sense of contribution to foster a productive collaboration.

Pathologists should not just be used for annotations and quick checks, but should be included in projects as equal contributors.In addition to Inti's University appointment she also is the president of the Swiss Digital Pathology Consortium (SDPath).
In 2018, a group of three professionals (Inti included:) in Switzerland founded the Swiss Digital Pathology Initiative (SDPI) to promote digital pathology and exchange knowledge. The initiative grew to over 140 members, and in 2021, SDPI collaborated with the Swiss Personalized Health Network to build a digital pathology network across Switzerland.The goal of SDPI is to harmonize and structure data by scanning cases, attaching a minimum set of variables to images, and using standardized hardware and formats. This network will allow researchers and industry partners to access virtual cohorts of patients for clinical trials, and the harmonized data sets can also help boost pharmaceutical development.

This would be the first initiative of this kind at a national level which will create a fantastic model for others to tweak and follow.
As a female in science in general and in the digital pathology field specifically, she has been fortunate to be surrounded by people who value her ideas and ideas of others, regardless of their gender. The gender gap in this field is still noticeable, particularly in more senior positions, which affects the number of female role models. Often insecurity can prevent some women from advancing, but exposure, experience and dedicated work on overcoming your own limitations will help. And so will involvement in initiatives such as SDPI.

THIS EPISODE'S RESOURCES:

• Paper: "Towards a national strategy for digital pathology in Switzerland"
• Swiss Digital Pathology Consortium website

And if we are not connected already, let's connect on LinkedIn!

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Computational pathology – how did this field even start?

In today’s episode my guest is Jeroen van der Laak, computational pathology professor at Radboud University Medical Center, who was recently listed on "The Pathologist Power List" in the category “Strange New worlds”

Jeroen has been in the field of computational pathology for over 30 years and has seen it being created and evolve.

He witnessed how advancements in whole-slide imaging and deep learning have allowed for the practical application of AI in pathology.

Throughout the evolution of the field of computational pathology the focus has shifted from research-oriented work to direct collaboration with clinicians to test AI in diagnostic practice.

During his tenure Jeroen has seen what it takes to be successful in the field of computational and digital pathology.

To be a successful researcher in this field you need to understand the importance of high quality data and understand how the field of pathology works and what you see in the tissue you are analyzing.

This is a very collaborative field and a responsibility of an AI researcher is making AI accessible and breaking down technical aspects for pathologists.

Jeroen co-leads the Computational Pathology Group at Radboud with two other researchers – Francesco Ciompi and Geert Litjens. Their criteria for choosing successful candidates for a digital pathology group include good team spirit, collaboration, willingness to learn, and understanding of the field.

It is just a matter of (not too much) time when AI will become mainstream in pathology labs and will improve the accuracy and speed of patient diagnosis. Just like whole slide scanning is becoming part of the routine pathology workflow, so will AI based image analysis.

THIS EPISODES RESOURCES

• This episode on YouTube
• "Bridging the gap between pathology and computer science" - full event on YouTube

And if we are not connected already, let's connect on LinkedIn!

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Although digital pathology was supposed to be faster and more seamless than classical pathology on glass there are still many manual steps in the workflow.

• Cleaning slides before scanning
• Loading the scanner
• Controlling the quality after scanning...

What if all this could be automated and all the manual work could be significantly reduced or even eliminated?

Well it can! With the 2nd generation of whole slide scanners powered with AI software, that can perform the tasks automatically during the scanning process.

And you don't even need to buy them to gain this benefit for your lab, because you can now buy digitization of your slides as a service from Pramana.

This episode's guest - Prasanth Perugupalli, the Chief Product Officer of Pramanaexplains exactly how it can be done and what was the journey to making it possible.

To learn more how it works and book a demo, visit:
https://pramana.ai/

THIS EPISODE'S RESOURCES:
Pramana's website

WOULD YOU LIKE TO LISTEN TO EXCLUSIVE, NON-CENSORED AND NON-POLISHED CONTENT?
Subscribe here

And if we are not connected already, let's connect on LinkedIn!

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Do you want to do tissue image analysis for FREE? 

Cytomine is your tool. But so are QuPath, Cell Profiler, ImageJ, and several … 

So how is Cytomine different? Cytomine focuses on collaboration (which is crucial in tissue image analysis projects!) and in addition to the free open-source version it also has a paid enterprise version.

In this broadcast my guest Gregoire Vicky, the co-founder of Cytomine will tell you what Cytomine is best for, what are the differences between the paid and free versions, and how it differs from QuPath and any other open-source tissue image analysis software.

THIS EPISODE'S RESPOURCES:
Cytomine (open source) website
Cytomine (commercial) website

OTHER EPISODES YOU MIGH LIKE:
QuPath - Open-Source quantitative pathology not only for pathologists w/ Pete Bankhead, University of Edinburgh

Join us for the HistoSuite webinar with Andrew Janowczyk
REGISTER HERE TODAY

And if we are not connected already, let's connect on LinkedIn!

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I started working in the digital pathology space, because it sounded cool.

When I started my digital pathology journey in 2016 as the first full time pathologist supporting the image analysis team, I thought it was the coolest job to get straight out of my veterinary pathology residency!

I was regarded as an expert (such a different feeling from what you experience during your training, when you are constantly being reminded how little you know and how much there still is to learn), which increased my confidence and motivated me to learn more. After all I needed to explain pathology to computer scientists.

Working together with the image analysis team and the software development team was exciting and I got to play and test software to view and annotate images.

Yes...
⛌ The images were shipped on hard drives
⛌ It took forever to open an image (over 30 sec...sometimes several minutes)
⛌ The annotation tool would regularly crash

FAST FORWARD 6 years

✔️No more hard drive shipping
✔️The speed of working with digital slides matches my speed at the microscope
✔️I didn't have to reboot my computer a single time today
✔️I work entirely remotely and can attend all recitals and events my kids take part in

VERY SELFISHLY I WOULDN'T WANT TO HAVE IT ANY OTHER WAY

I know I'm part of a minority of privileged pathologists. But it very much reminds me of the time when smartphones came to the market, when I could not afford one yet and they did not have so many functionalities

I was dreaming of having one that could always connect to the Internet, so that I could use Google Maps whenever I wanted (both on vacation and during my commute as a Polish PhD student studying in Germany - knowing the fastest way home on the weekend and avoiding traffic would be priceless!)

NOW EVERYONE HAS A SMARTPHONE

And would you want to have a different phone? The old one?
I know some would, but THEY ARE A MINORITY NOW.

How far are you in your digital pathology journey?
How do you feel about it? Is it already a reality or still a science fiction for you?
Let me know in the comments on LinkedIn

THIS EPISODE'S RESOURCES:
Digital Pathology Starter Kit + Digital Pathology Newsletter
"I started because it was cool..." - LinkedIn post

And if we are not connected already, let's connect on LinkedIn!

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Several scanners have been cleared by the FDA for clinical pathology work, but what about FDAs stand on all the nonclinical pathology work done in a regulatory environment? Specifically the work done in the Good Laboratory Practice (GLP) compliant environment?

• Can we use the slides without restrictions in lieu of glass slides?
• What part of the digital pathology system do we need to validate?
• How do wemaintain and archive the whole slide images used for the pathology portion of the nonclinical toxicologic studies?

Good news!

There is an official FDA draft guidance for the industry that asks all those and a few more questions and answers them at the same time.

In this episode I will go through the guidance for you, so that you don't have to spend time reading this document. But if you feel like doing it anyway, it's available for you to download below in this episode's resources.

And in case you want to skip the whole episode (which I sincerely hope you don't! Believe me, it's pretty fun for and FDA guidance episode:), the answer to most questions is YES.

Talk to you inside the episode!

This episode's resources:

• Use of Whole Slide Imaging in nonclinical Toxicology Studies: Questions and Answers. Draft Guidance for the Industry.
• Dr. Aleks Zuraw on TikTok :)

And if we are not connected already, let's connect on LinkedIn!

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As much as I love Digital Pathology - things that are not always perfect, and the integrations of systems are not always seamless. We don't need to sugar coat it.

And the sooner we start talking about the things that are not so cool, the sooner we will be able to change them.

In this podcast episode I discuss the things that need to be improved with Puneet Pantane, the Co-Founder and Chief Marketing Officer of Crosscope, where he leverages the power of new technologies such as AI, machine learning, and image processing to improve the research, diagnosis and treatment of cancer.

In this episode we cover:

• What is Crosscope? Where is this company and what are they actually doing?
• What is digital transformation?
• Who are Crosscope's customers?
• What is not working in digital pathology?
• If we had a magic wand that can solve any digital pathology problem, what would NUMBER 1 PROBLEM to solve be?
  • Spoiler alert: Puneet - interoperability of systems
  • Aleks - reinventing the wheel in image analysis
• What digital pathology problems can be fixed immediately (the low hanging fruits)?
• How to standardize digital pathology in small pieces?

If you want to learn more about Crosscope, click here

And if we are not connected already, let's connect on LinkedIn!

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This episode is brought to you by Aiforia. Thank you Aiforia :)

Today you will learn how Raish Pai, MD, a busy, practicing pathologist from Mayo Clinic developed a complex supervised deep learning tissue image analysis model to quantify visual diagnostic features of colon cancer and in the process developed a model that can predict clinical outcome.

He used the deep learning-based tissue image analysis platform - Aiforia.

The quantified features included:

• Stromal immune cell Infiltrates
• Immature stroma
• Tumor-Infiltrating Lymphocytes
• Mucin
• Different growth patterns
• & many others

THIS EPISODE'S RESOURCES:

• Podcast with Thomas Westerling-Bui, Aiforia: "What is Validation and how to validate and AI image analysis solution"
• Rish Pai's publication "Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival"
• Colon Cancer Family Registry website
• Podcast about the BIG PICTURE initiative "BigPicture - the largest whole slide repository for AI model development in pathology. Where do we stand at month 15/ 72?"

THIS EPISODE'S SPECIAL OFFER "THE BETA COHORT" **Join and be part of the co-creation of the only online course like this in the digital pathology world "PATHOLOGY 101 FOR TISSUE IMAGE ANALYSIS".

Learn more about the AMAZING OFFER that awaits you when you join the BETA COHORT today!*!!! Limited time offer!!! The discount expires on November 27th 2022*

Learn more HERE

Let's connect on LinkedIn!

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This episode is brought to you by Hamamatsu. Thank you Hamamatsu :)

So...you are already doing digital pathology in your institution but would like to scale it, take it to the next level? How do you do it, where do you start?

In this episode my guest, Mark Zarella, PhD (previously Johns Hopkins University, currently Mayo Clinic) explains how he did exactly that at Johns Hopkins University.

He talks about:

• What is important when evaluating whole slide scanners and how to choose the best whole slide scanner for you
• How he organized and managed the whole slide images at Johns Hopkins University
• How he scaled the operations from ca. 10K slides to ca. 750K slides a year
• How he ensured interoperability of systems
• How he approached automated slide quality control

AND MUCH MUCH MORE!

If you are serious about taking your digital pathology operations to the next level, THIS IS THE EPISODE TO LISTEN TO!

THIS EPISODE'S RESOURCES

Mark's Paper: "High-throughput whole-slide scanning to enable large-scale data repository building"

Blog post: HOW TO CHOOSE A WHOLE SLIDE IMAGING SCANNER FOR DIGITAL PATHOLOGY – THE ULTIMATE GUIDE

Podcast episode: HOW TO CHOOSE A WHOLE SLIDE IMAGING SCANNER FOR DIGITAL PATHOLOGY W/ DOUG STAPLETON, HAMAMATSU

Let's connect on LinkedIn!

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As the digital pathology community is embarking on the journey of DICOM implementations questions we haven't asked ourselves arise...

• Is DICOM and image format or is it a standard? What is the difference?
• Are all the DICOM images the same or do they differ?
• how do the differences influence the technology developments and workflows?
• Is there a single best way of implementing DICOM or do we need to keep iterating?
• And who can help us on this journey?

Who would be a better guest to talk about it than the DICOM standard editor himself, Dr. David Clunie?

This podcast episode is a recording of a live broadcast we had together recently where he answers all the abovementioned questions and some more!

If you are thinking of using or implementing DICOM for your digital pathology journey, be sure to listen to this episode!

THIS EPISODE'S RESOURCES:

• DICOM pathology whole slide image repository (click on the histopathology image to access)
• Contact David Clunie at: dclunie@dclunie.com
• Watch on YouTube

OTHER EPISODES YOU MIGHT LIKE:

• DICOM standard for pathology annotations. Why do we need it? W/ David Clunie, PixelMed Publishing

Let's connect on LinkedIn!

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Join me for the FREE Independent Digital Pathology Event "Bridging the Gap Between Pathology and Computer Science" 👇

https://bit.ly/BridgingTheGap1

Let's connect on LinkedIn!

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This episode's guest, Dr. Kate Baker, a veterinary clinical pathologist, developed a smartphone app for veterinary telecytology! This digital pathology smartphone app is called Pocket Pathologist and let's you get access to a veterinary pathologist opinion remotely.

This technology can be used for other areas of static telepathology including rapid on site evaluation (ROSE) and Dr. Kate is giving us a sneak peek into the app development and how it was for a veterinarian to work with an app development team (and NO, it does not cost a million dollars).

So don't hesitate to check it out: https://www.pocketpathologist.com/

THIS EPISODE'S RESOURCES:

• PREVIOUS PODCAST EPISODE WITH KATE: Digital veterinary cytology and social media teaching w/ Kate Baker, Veterinary Cytology Schoolhouse
• KATE'S WEBSITE - VETERINARY CYTOLOGY SCHOOLHOUSE
• KATE'S VETERINARY CYTOLOGY ONLINE COURSES
• SKOPED MICRO PHONE ATTACHMENT
• IMPACT OF PHOTOGRAPHER EXPERIENCE AND NUMBER OF IMAGES ON TELECYTOLOGY ACCURACY. (Veterinary Clinical Pathology - Journal Article)
• HOW TO TAKE STUNNING MICROSCOPIC IMAGES WITH YOUR PHONE - DOWNLOADABLE GUIDE

Let's connect on LinkedIn!

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This is a joint podcast episode where the hosts of "Beyond the Scope" - the official Digital Pathology Association podcast and the host of the "Digital Pathology Podcast" meet to talk about what is going on in our discipline.

Together David Tulman, Giovanni Lujan, and Aleksandra Zuraw cover the current digital pathology topics such as digital pathology guidelines for clinical and non-clinical pathology, digital pathology adoption in clinical pathology settings and pharmaceutical pathology as well as how the pandemic influenced the adoption of digital pathology.

This episode is different than most of our episodes and it is really fun to listen to so stay till the end!

THIS EPISODE'S RESOURCES

YouTube Version of THIS episode is here.

Digital Pathology Podcast episode with David Tulman, Instapath:

AUDIO: https://digitalpathologyplace.com/pod...

VIDEO: https://www.youtube.com/watch?v=wECId...

Podcasts with Chen Sagiv, DeePathology:

Digital pathology Podcast: https://youtu.be/DrEUnkYkN28

Beyond the Scope: https://podcasts.apple.com/no/podcast...

Podcasts with David Clunie, Pixelmed Publishing:

Digital Pathology Podcast: https://digitalpathologyplace.com/pod...

Beyond the Scope: https://podcasts.apple.com/us/podcast...

Pathology Visions 2022 registration page: https://digitalpathologyassociation.o...

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Have you ever wondered what semi-supervised, weekly, and unsupervised artificial intelligence digital pathology models can do to help pathologists?

Can we finally stop annotating???

This episode's guest Geert Litjens - a member of the computational pathology group at Radboud University Medical Center explains how semi-supervised and weekly supervised artificial intelligence-based image analysis can help pathologists do better, more time-efficient, and data-efficient digital pathology.

The supervised deep learning image analysis methods are used often and are well accepted in the digital pathology scientific community, however, they rely heavily on whole slide image annotations. This is very time-consuming and is subjected to annotator to annotator variability.

There has been a lot of research going on in the computational pathology community on the semi and weakly supervised approaches. It turns out that those approaches are starting to match the results delivered by the supervised approaches.

Are we there yet? Can we stop annotating pathology slides altogether and rely on the slide-level labels?

Listen to the full episode to learn more + share with friends!

This episodes resources:

1. Aiosyn website
2. StreamingCNN
3. Pathology streaming pipeline
4. Streaming CNNs for Multi-Megapixel Images (article)
5. DALL-E-2 network that generates artworks from descriptions in natural language

Other podcast episodes you'll enjoy:

1. Bigpicture - the largest whole slide repository for AI model development in pathology. Where do we stand at month 15/72?
2. 5 Ways to make histopathology image models more robust to domain shift w/ Heather Couture, Pixel Scientia Labs

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There are a few websites online other than the Digital Pathology Place that talk about different aspects of digital pathology. An important one being Pathology News.

Pathology News is an online place bringing together the digital pathology vendors and purchasers. It is meant to be a single community for everyone working in the digital pathology space. A community working together on advancing the digital pathology science.

A unique (and my favorite!) feature of the website, NOT AVAILABLE ANYWHERE ELSE ONLINE, is a special page where digital pathology users have access to detailed information about available digital pathology solutions provided by the digital pathology vendors.

It is like a 24/7 online digital pathology conference where the users can visit vendor space any time they need a specific piece of information, and they can visit multiple vendor spaces and compare their solutions.

All from the comfort of their home without having to call or interact with a single vendor representative before they are ready.

This space is called the Technology Buyers guide.

In addition to this unique vendor-purchaser interactive tool Pathology News has other elements:

• scientific articles
• latest digital pathology news
• list of upcoming digital pathology events
• digital pathology career section with the latest vacancies

and now also...[drum roll please]......

A PODCAST SECTION with the DIGITAL PATHOLOGY PODCAST

We partnered to serve the largest audience possible

Digital Pathology Place and Pathology News are both on a mission to advance digital pathology in the scientific community and we want to serve the largest audience possible. We are doing it in a very complementary way that builds bridges within the multidisciplinary environment of digital pathology.

This is why we partnered to make the Digital Pathology Podcast available to the Pathology News readers straight from the Pathology News website and from their mailbox for those who are subscribed to the monthly newsletter.

Listen to the full episode to meet Jonathon Tunstall, the CEO of Pathology News and learn what else you can find on the website.

This episode's resources:

• Pathology News website
• Pathology News Technology Buyers guide
  • Scan and capture
  • Image and data management
  • Quantitative image analysis and AI
  • Contract services
• Pathology News Podcast section

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Deep learning artificial intelligence has entered the field of digital pathology and is here to stay because it consistently outperforms the classical image analysis methods on pathology slides.

The only caveat is that to train good deep learning image analysis models we need a lot of whole slide images.

Where do we get them? Is there a central repository that can be used for this purpose?

Good news, there is one in the making!

There is an ongoing project to create a very large repository of several million whole slide images accessible for the digital pathology community. It is called BIGPICTURE and in this podcast you will learn about it from the experts.

This episode's guests, two of the project leaders - Julie Boisclair from Novartis and Jeroen van der Laak from the computational pathology group at RadboudUMC are explaining the

• who
• what
• how and
• when of the BigPicture project.

Listen to the full episode to learn all about it.

This episode's resources:

• BigPicture project website
• IMI - BigPicture: A Central Repository for Digital Pathology - publication

Episodes you might also like:

• 5 ways to make histopathology image models more robust to domain shift w/ Heather Couture, Pixel Scientia Labs
• Why Machine learning expertise is needed for digital pathology projects w/ heather Couture, Pixel Scientia Labs

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Digital pathology is supposed to help pathologists provide better patient care and make their lives easier, but what about other doctors, do they even care? Maybe radiologists? Oncologists? Nope…Dermatologists! They do care!

And they are the clients of Pathology Watch – a CLIA lab specializing in dermatopathology, that is currently servicing samples from over 65 dermatology clinics in the USA.

Pathology Watch provides an end-to-end digital pathology solution for dermatologists. From processing the samples sent by the dermatologists, through the dermatopathology report to the whole slide image of the diagnosed sample, and all this browser-based and integrated with the dermatology clinic’s electronic medical record (EMR) systems. This provides a completely non-disruptive workflow.

Pathology Watch is providing a true end-to-end solution built around dermatologists.

The EMR integration saves the dermatologists time (25h/ month!!! Who would not want to have that?!?) and whole slide images build the bridge between and improve the communication on the “patient-dermatologist-pathologist” line.

Dermatologists can show the images of the cases to the patients, and they can see the highlighted areas used by the pathologist when diagnosing the case.

Once the digitization and intersystem integration take care of the time savings it’s time to step up the game! The next step is using artificial intelligence for better dermatopathology diagnostics and to gain even more time savings. Pathology Watch designed its AI pipeline specifically to bypass the known industry problem of generalizability of AI models.

It is extremely difficult to train generalizable models on samples from different institutions, but if the samples are processed in just one lab in a very controlled environment, using automated equipment and performing rigorous quality control, the pre-analytical variability causing a lack of generalizability is taken care of.

As in any digital pathology operation, troubleshooting is part of the business. What do you do when your scanner breaks down? How do you store the digital pathology images effectively in a cost-efficient way? And how do you deliver the slides in the browser FAST?

It took the Pathology Watch team a few years to solve those and other challenges and come up with good mitigation strategies.

Do you want to know how they did it? Listen to the full episode to learn more from Dan Lambert, the CEO of Pathology Watch.

This Episodes resources

Pathology Watch website

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A common misconception about digital pathology is that it is synonymous with whole slide imaging and has a high price point. This is not the case, as one can enter the digital pathology world and benefit from what it has to offer with a simple microscope camera. If we would like a more sophisticated solution, but don't want to get a whole slide scanner or don't have the use case or business case to justify it, no worries, there is enough to choose from!

In this episode, Mike Miller from I.Miller Microscopes is taking us through all the different levels and price points of digital pathology solutions from a simple microscope camera to a whole slide scanner explaining everything in between.

The digital pathology solutions discussed in this episode include:

1. Simple microscope camera + imaging software for image capture
   • For teaching and learning
   • For capturing static images
     • presentations
     • publications
     • tumor boards
   • For screen sharing on communication platforms (e.g. zoom, teams, etc.)
2. Microscope camera with a network port allowing for live streaming
   • For intraoperative evaluation by an on-site or off-site pathologist
     • fine-needle aspirates
     • frozen sections
3. Live remote control telepathology system
   1. Live remote control telepathology system with low throughput screening capabilities (aka hybrid system)
      • For use in remote areas without access to pathologists
4. Whole slide scanners
   • For high throughput digital pathology workflows on formalin-fixed paraffin-embedded (FFPE) material

Listen to the full episode to learn the details and the price points of each solution!

This episode resources:

• I. Miller Microscopes website
• Microscope central website
• Path 4k - high-resolution microscope camera

Episodes you might also like:

• Digital pathology in a suitcase. How Grundium's portable whole slide scanning microscope expands the reach of telepathology w/ Mika Kuisma, Grundium

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This episode is brought to you by Visiopharm.

With the regulatory approvals of whole slide imaging systems, digital pathology became the modality for routine diagnostics. Digitalization of pathology is aiming at increasing precision and productivity in the pathology lab, but the adoption of this field is slower than expected.

One of the causes of the slow adoption is that going digital in a pathology lab means a much bigger investment than just the cost of the whole slide scanners for slide digitization. Additional costs include digital storage and infrastructure, slide and workflow management, and connectivity to lab information systems.

Because the improvements in precision and productivity gained by going digital are modest at best, a higher value is expected from image analysis and artificial intelligence.

The research and diagnostic applications of image analysis have been explored for decades already and many have found great use in the research-diagnostics continuum. However, a large need for the standardization of tissue diagnostic assays remains unmet.

Standardization of the staining and of the diagnostic interpretation of tests would tremendously benefit pathology and patient care. So far, the standardization efforts focused on the interpretation part of the puzzle. Several quantification algorithms have been developed, many of which received regulatory clearance. At the same time, the IHC assays on which the algorithms are based often lack standardization, and this is where more effort should be put.

Currently, only pathology institutions that go fully digital reap the digital pathology benefits. There is not an efficient way to start slowly, rather it seems to be “all or nothing”. Enabling institutions to embark on the digital pathology journey in an incremental fashion would change the digital pathology landscape and significantly increase the adoption of this technology.

The more value on different fronts digital pathology can provide to institutions and patients, the more the adoption will increase. And we have not yet explored all the ways in which value can be provided.

Listen to the full episode to learn about it in more detail and visit Visiopharm’s website, to learn how they are contributing to the digital transformation in pathology.

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Visiopharm is a company offering image analysis software used on pathology images. The software has been on the market for over 20 years and has evolved through the transition of digital microscopy to digital pathology. Digital microscopy provided static tissue images captured through the microscope camera and only branched out into digital pathology with the wider availability and adoption of whole slide scanners. Image analysis spans digital microscopy and digital pathology, and image analysis methods had to evolve in parallel with the imaging technologies to address the hypercomplex pathology problems. 

The complexity of digital pathology problems and research questions increases with every additional stain and scientific discovery. Visiopharm’s team challenged themselves by providing an image analysis solution capable of addressing this hypercomplexity and enabling researchers to advance scientifically with their tool. 

Artificial Intelligence (AI) capabilities applied to computer vision problems took tissue image analysis to a whole new level and incorporating AI into the Visiopharm software tremendously increased the accessibility of this method. 

In addition to the two well-known technologies that enabled digital pathology breakthroughs (whole slide imaging and AI), two other important advancements happened during the last two decades

·       emphasis on interoperability between different digital pathology systems 

·       advances in the field of data visualization. 

Together these four components are driving the progress of digital pathology both on the diagnostic and research front. 

During the last 20 years Visiopharm grew significantly, both organically and through funding and they continue creating value and powerful image analysis tools for the tissue image analysis community. 

Listen to the full episode with Visiopharm’s CEO Michael Grunkin to learn more about this 20-year perspective on what happened in the field of digital microscopy and digital pathology.