The role of machine learning in clinical research: transforming the future of evidence generation
Name
13063_2021_Article_5489.pdf
Size
1003.98 KB
Format
Adobe PDF
Checksum (MD5)
5e2232ccbed662328b26ee24f8874edf
Author(s)
Weissler, E. H.
Naumann, Tristan
Andersson, Tomas
Ranganath, Rajesh
Elemento, Olivier
Luo, Yuan
Freitag, Daniel F.
Benoit, James
Hughes, Michael C.
Khan, Faisal
Date Issued
August 16, 2021
Publisher
BioMed Central
Citation
Trials. 2021 Aug 16;22(1):537
Version
Final published version
Abstract
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s13063-021-05489-x
