Future of clinical research - AI & Automation in clinical trials

“The future of Clinical Research is AI”! It’s commonplace to hear this nowadays 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.

- Manuj vangipurapu

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.

Data Management Data Management offers tremendous scope for AI enabled automation. Some of them are listed below:

Data Analysis Machine Learning can provide many insights into clinical data during and after the trial.

Classification, clustering and prediction are some of the techniques which can be used in data analysis to bring out critical insights into large datasets. Patient behavior, adverse events etc. can be predicted using machine learning.

Regulatory Submission Regulatory submission in clinical trials requires a large amount of documentation. These can be templatized and automated using machine learning.

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