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 f...

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Published in:Current controlled trials in cardiovascular medicine Vol. 22; no. 1; pp. 537 - 15
Main Authors: Weissler, E. Hope, Naumann, Tristan, Andersson, Tomas, Ranganath, Rajesh, Elemento, Olivier, Luo, Yuan, Freitag, Daniel F., Benoit, James, Hughes, Michael C., Khan, Faisal, Slater, Paul, Shameer, Khader, Roe, Matthew, Hutchison, Emmette, Kollins, Scott H., Broedl, Uli, Meng, Zhaoling, Wong, Jennifer L., Curtis, Lesley, Huang, Erich, Ghassemi, Marzyeh
Format: Journal Article
Language:English
Published: London BioMed Central 16.08.2021
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Artificial Intelligence
Biomedicine
Breast cancer
Clinical trials
Clinical trials as topic; Machine learning
Collaboration
Commentary
Datasets
Health Sciences
Humans
Machine Learning
Mathematical models
Medical research
Medicine
Medicine & Public Health
Medicine, Experimental
Methods
Natural language processing
Neural networks
Patients
R&D
Regulatory approval
Research & development
Research design
Research ethics
Statistics for Life Sciences
Technology application
United States
United States Food and Drug Administration
United States
Research ethics
Research design
Clinical trials as topic; Machine learning
Artificial intelligence
ISSN:1745-6215, 1745-6215
Online Access:Get full text
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Summary: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.
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ISSN:1745-6215
1745-6215
DOI:10.1186/s13063-021-05489-x