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Creation and Adoption of Large Language Models in Medicine

To identify the key insights or developments described in this article
1 Credit CME
Abstract

Importance  There is increased interest in and potential benefits from using large language models (LLMs) in medicine. However, by simply wondering how the LLMs and the applications powered by them will reshape medicine instead of getting actively involved, the agency in shaping how these tools can be used in medicine is lost.

Observations  Applications powered by LLMs are increasingly used to perform medical tasks without the underlying language model being trained on medical records and without verifying their purported benefit in performing those tasks.

Conclusions and Relevance  The creation and use of LLMs in medicine need to be actively shaped by provisioning relevant training data, specifying the desired benefits, and evaluating the benefits via testing in real-world deployments.

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CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.

Article Information

Accepted for Publication: July 11, 2023.

Published Online: August 7, 2023. doi:10.1001/jama.2023.14217

Corresponding Author: Nigam H. Shah, MBBS, PhD, Center for Biomedical Informatics Research, Stanford University, 3180 Porter Dr, Palo Alto, CA 94305 (nigam@stanford.edu).

Author Contributions: Dr Shah had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Drafting of the manuscript: Shah, Pfeffer.

Critical review of the manuscript for important intellectual content: All authors.

Administrative, technical, or material support: Entwistle, Pfeffer.

Supervision: Pfeffer.

Conflict of Interest Disclosures: Dr Shah reported being a co-founder of Prealize Health (a predictive analytics company) and Atropos Health (an on-demand evidence generation company). No other disclosures were reported.

Additional Contributions: We acknowledge the members of the data science team at Stanford Health Care for helpful discussions to refine the arguments made in this article. We acknowledge Jason Fries, PhD, and Alison Callahan, PhD (both with Stanford University), for help in creating the first draft of the Figure; they were not compensated for their contributions.

AMA CME Accreditation Information

Credit Designation Statement: The American Medical Association designates this Journal-based CME activity activity for a maximum of 1.00  AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to:

  • 1.00 Medical Knowledge MOC points in the American Board of Internal Medicine's (ABIM) Maintenance of Certification (MOC) program;;
  • 1.00 Self-Assessment points in the American Board of Otolaryngology – Head and Neck Surgery’s (ABOHNS) Continuing Certification program;
  • 1.00 MOC points in the American Board of Pediatrics’ (ABP) Maintenance of Certification (MOC) program;
  • 1.00 Lifelong Learning points in the American Board of Pathology’s (ABPath) Continuing Certification program; and
  • 1.00 credit toward the CME of the American Board of Surgery’s Continuous Certification program

It is the CME activity provider's responsibility to submit participant completion information to ACCME for the purpose of granting MOC credit.

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