Enhancing Postmarketing Surveillance of Medical Products With Large Language Models
LLMs represent a novel paradigm that may facilitate generation of information to support medical product postmarket surveillance activities that have not been possible. However, additional work is required to ensure LLMs can be used in a fair and equitable manner, minimize false positive findings, and support the necessary rigor of signal detection needed for regulatory activities.
Authors
- Mohammed Ali Al-Garadi (ORCID: https://orcid.org/0000-0002-6991-2687)
- Rishi Desai (ORCID: https://orcid.org/0000-0003-0299-7273)
- Michael E. Matheny (ORCID: https://orcid.org/0000-0003-3217-4147)
- Keith A. Marsolo (ORCID: https://orcid.org/0000-0002-4416-1549)
- Daniel Fabbri (ORCID: https://orcid.org/0000-0003-0530-2510)
- Robert Ball (ORCID: https://orcid.org/0000-0002-1609-7420)
- Kevin B. Johnson (ORCID: https://orcid.org/0000-0003-1016-9363)
- Sharon E. Davis (ORCID: https://orcid.org/0000-0003-0792-8867)
- Colin G. Walsh (ORCID: https://orcid.org/0000-0002-9379-2056)
- Jie Yang (ORCID: https://orcid.org/0000-0001-5696-363X)
- Gerald J. Dal Pan (ORCID: https://orcid.org/0000-0003-4874-5864)
- Joshua Smith (ORCID: https://orcid.org/0000-0003-2661-3203)
- Ruth Reeves
Institutions
- Brigham and Women's Hospital (US)
- United States Food and Drug Administration (US)
- Center for Drug Evaluation and Research (US)
- Harvard University (US)
- Duke University (US)
- Vanderbilt University (US)
- VA Tennessee Valley Healthcare System (US)
- University of Pennsylvania (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2024-08-16
- DOI
- https://doi.org/10.1001/jamanetworkopen.2024.28276
- Citations
- 26
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 6.08