The role of machine learning in clinical research: transforming the future of evidence generation
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...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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BioMed Central
2021
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| Online Access: | https://hdl.handle.net/1721.1/136869 |
| _version_ | 1826212812423168000 |
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| author | Weissler, E. H. 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 |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Weissler, E. H. 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 |
| author_sort | Weissler, E. H. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:38:38Z |
| format | Article |
| id | mit-1721.1/136869 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:38:38Z |
| publishDate | 2021 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | mit-1721.1/1368692024-03-20T19:05:37Z The role of machine learning in clinical research: transforming the future of evidence generation Weissler, E. H. 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 Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Institute for Medical Engineering & Science 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. 2021-11-01T14:33:53Z 2021-11-01T14:33:53Z 2021-08-16 2021-08-22T03:11:03Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136869 Trials. 2021 Aug 16;22(1):537 PUBLISHER_CC en https://doi.org/10.1186/s13063-021-05489-x Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf BioMed Central BioMed Central |
| spellingShingle | Weissler, E. H. 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 The role of machine learning in clinical research: transforming the future of evidence generation |
| title | The role of machine learning in clinical research: transforming the future of evidence generation |
| title_full | The role of machine learning in clinical research: transforming the future of evidence generation |
| title_fullStr | The role of machine learning in clinical research: transforming the future of evidence generation |
| title_full_unstemmed | The role of machine learning in clinical research: transforming the future of evidence generation |
| title_short | The role of machine learning in clinical research: transforming the future of evidence generation |
| title_sort | role of machine learning in clinical research transforming the future of evidence generation |
| url | https://hdl.handle.net/1721.1/136869 |
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