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The Covid-19 pandemic has pushed the global clinical trial industry and regulators to rapidly adopt technology and accelerate clinical development. Along with other technologies, adoption of AI & ML has moved forward at a much more rapid pace than initially envisioned. Similar to remote technologies, AIML technologies have gone beyond user’s expectations and have quickly occupied a central position in an organisation’s plans for the future. Pfizer’s 45,000 subject Covid-19 vaccine study which was completed in record time with the help of AIML is a case in point.

Looking at the big jump in the number of sessions in SCDM 2021 compared to the sessions in 2020 gives us a very good idea about the central role being played by AIML in clinical trials of both today and tomorrow.

Data Managers across are rightly concerned about the role they have to play in this increasingly digital world where every new use case appears to threaten their jobs however much they are reassured that would not be the case.

As a clinical trial industry professional who designs software solutions for the industry, I decided to understand the buzz around AI and hopefully understand it well enough to incorporate it into our products and develop new products around it. I decided early on that the courses being offered online would not be adequate for a serious practitioner and for someone who wanted to design solutions.

I decided to do a certification in AIML from the International Institute of Information Technology, Hyderabad. The course is highly regarded and is very technical, covering the entire gamut of machine learning including developing various models, Natural Language Processing, MLP and Deep Learning. My learnings from the course and advice to Clinical Trial Data Managers who are deciding to learn AI & ML are as follows:

AI is just getting started

AI models and computations are evolving rapidly and are changing on a daily basis. The good thing about AI is that the models are being made available to regular users almost as soon as they are done and validated. This is very different from other technologies where there is usually a lag of about six months to a year from the lab to widespread usage. The other good thing is that the validated models are made publicly available and in the form of libraries for non-technical users to also deploy

Future of Jobs in the Era of AI

All of this means that AI is already here and its already impacting jobs and that there’s very little lead time for Data Managers to adapt and up-skill themselves.

For Junior Data Managers

Who are just starting out and who have been in the industry only for a few years, my advice is to learn Python (if possible) and do a Machine Learning course which is technical in nature. The course would need to be comprehensive and cover all the various models as well as MLP and Deep Learning. The focus is on technical knowledge and not business knowledge. These courses are offered by the various IIITs, IITs and IIMs. Online courses on Coursera and Udemy may be inadequate for the level of knowledge and rigour required.

For Mid-level to Senior Data Managers

Business Analytics courses consisting of Machine Learning would be appropriate. Senior managers’ role would be to identify use cases in the clinical trial process – from study design, site section, data capture, study monitoring, data management, coding to data analysis and submission. A thorough understanding of the business use case, data preparation and model selectio

Is the role of Clinical Data Managers changing with the entry of AI and ML? What does the future holds for CDMs. Check the blog to find now!

By Manuj vangipura, CEO, Clinion

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“The future of Clinical Research is AI”! It’s common place to hear this now a days but what does it mean? We have all heard of how AI is being applied in basic research in identifying molecules, in finding disease patterns in potential patient populations and in Virtual Trials. In this article, I will briefly touch upon the various well known and a few lesser-known applications of AI and Automation in the clinical trials process.

Machine Learning (ML) is a branch of AI which deals with applying algorithms to data, enabling the system to ‘learn’ and improve. ML allows users to process large quantities of data and make smart inferences and predict outcomes. These insights can be used to automate parts of the system leading to a faster and a more efficient clinical trial system. Automation allows the ML predictions to be fed back into the system and specific actions to be taken, reducing the need for human intervention, and improving quality and speed. ML and automation can be applied across every stage of the trial process.

Study Design

Machine Learning can be applied to protocol design and language translation. Using existing protocol data and health libraries for specific therapeutic areas, a protocol for a new study can be generated by the system. The ML algorithms would be able to design an optimal protocol from the knowledge base, leading to reduced design times and protocol amendments and study disruptions. Language translation could also be done quickly and easily and with a greater degree of accuracy than traditional methods since the ML model would have a domain specific language knowledge base to learn from.

Study Setup

ML can be used to automate the design and set up of the case report form and study database. Using a library of CRFs for specific therapies and study designs, based on the protocol, the ML model can be trained to design an optimal CRF along with edit checks. Automation allows this output to be translated into actual study setup and validation, allowing database designers to tweak the design as and where required. This approach leads to an optimal design which also incorporates edit checks which otherwise might be missed out if being designed by a human. Automation also allows this ML designed study to be set up and validated. The validation report provides the necessary inputs to designers to apply the finishing touches before go-live. ML can also be used to automate SDTM mapping or create SDTM annotated studies.

Trial Management

A lot of automation involving machine learning is possible in trial management. Some of the obvious use cases are site selection, patient enrolment, Risk Based Monitoring (RBM) and Chatbots.
Site Selection: Optimal site selection is possible using machine learning models. These models can be trained to review site parameters such as Enrolment, Safety, Compliance and Data Quality and predict which sites would be good candidates for a new study for a particular specialty. Prioritization of these parameters depends upon type of trial and CRO/Sponsor. The algorithm could be trained on previous study data and would be able to predict site performance for a new study

Patient Enrolment: Predictive Analytics for patient enrolment is a popular use case. This utilizes variables such as therapeutic area, study duration, disease prevalence (from Health Economics), study complexity, adverse events, randomization, multi-centric etc. The ML algorithm would review all the above variables and select those which have the most impact (relevant). The finalized model could then be used for future studies to predi

Is the role of Clinical Data Managers changing with the entry of AI and ML? What does the future holds for CDMs. Check the blog to find now!

By Manuj vangipura, CEO, Clinion

View Details

Clinion eCOA/ePRO software enables you to run Decentralized clinical trials with patients participating remotely, using eConsent and patient diary for higher compliance.

About Clinion
Clinion is a life sciences technology company offering innovative software solutions in 
the pharmaceutical industry since 2010. Our first product, also called Clinion, is an 
integrated eClinical trial platform for small and medium CROs, academic research 
organisations and pharmaceutical companies.