Regulatory Considerations on the use of Machine Learning based tools in Clinical Trials

Abstract

Background: The widespread increasing use of machine learning (ML) based tools in clinical trials (CTs) impacts the activities of Regulatory Agencies (RAs) that evaluate the development of investigational medicinal products (IMPs) in clinical studies to be carried out through the use of data-driven technologies. The fast progress in this field poses the need to define new approaches and methods to support an agile and structured assessment process. Method: The assessment of key information, characteristics and challenges deriving from the application of ML tools in CTs and their link with the principles for a trustworthy artificial intelligence (AI) that directly affect the decision-making process is investigated. Results: Potential issues are identified during the assessment and areas of greater interaction combining key regulatory points and principles for a trustworthy AI are highlighted. The most impacted areas are those related to technical robustness and safety of the ML tool, in relation to data used and the level of evidence generated. Additional areas of attention emerged, like the ones related to data and algorithm transparency. Conclusion: We evaluate the applicability of a new method to further support the assessment of medicinal products developed using data-driven tools in a CT setting. This is a first step and new paradigms should be adopted to support policy makers and regulatory decisions, capitalizing on technology advancements, considering stakeholders' feedback and still ensuring a regulatory framework on safety and efficacy.

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