The role of machine learning in clinical research: transforming the future of evidence generation

Background Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum. Results Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas. Conclusions ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence.

Automated summary

This manuscript reviews a multi-stakeholder conference discussing the current and future state of using machine learning in clinical research, highlighting the potential and priority areas for ML in clinical trial design, implementation, and analysis. Conference attendees included biomedical and ML researchers, FDA representatives, AI companies, patient advocacy groups, and pharmaceutical companies, emphasizing the role of ML in clinical research and the barriers it faces, with a noted lack of peer-reviewed evidence in several areas.