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Appl Clin Inform 2025; 16(05): 1738-1748
DOI: 10.1055/a-2735-0527
Research Article

Physician Perspectives on Large Language Models in Health Care: A Cross-Sectional Survey Study

Authors

  • Hyo Jung Hong

    1   Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, California, United States

  • Nigam H. Shah

    2   Clinical Excellence Research Center, Stanford University, Stanford, California, United States
    3   Department of Medicine, Stanford University, Stanford, California, United States
    4   Technology and Digital Solutions, Stanford Health Care, Palo Alto, California, United States

  • Michael A. Pfeffer

    3   Department of Medicine, Stanford University, Stanford, California, United States
    4   Technology and Digital Solutions, Stanford Health Care, Palo Alto, California, United States

  • Lisa S. Lehmann

    5   Department of Medicine, Mass General Brigham and Harvard Medical School, Boston, Massachusetts, United States
    6   Department of Health Care Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States

Funding None.
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Abstract

Objectives

This study aims to evaluate physicians' practices and perspectives regarding large language models (LLMs) in health care settings.

Methods

A cross-sectional survey study was conducted between May and July 2024, comparing physician perspectives at two major academic medical centers (AMCs), one with institutional LLM access and one without. Participants included both clinical faculty and trainees recruited through departmental leadership and snowball sampling. Primary outcomes were current LLM use frequency, ranked importance of evaluation metrics, liability concerns, and preferred learning topics.

Results

Among 306 respondents (217 attending physicians [70.9%], 80 trainees [26.1%]), 197 (64.4%) reported using LLMs. The AMC with institutional LLM access reported significantly lower liability concerns (49.2 vs. 66.7% reporting high concern; 17.5 percentage points difference [95% CI, 6.8–28.2]; p = 0.0082). Accuracy was prioritized across all specialties (median rank 1.0 [interquartile range; IQR, 1.0–2.0]). Of the respondents, 287 physicians (94%) requested additional training. Key learning priorities were clinical applications (206 [71.9%]) and risk management (181 [63.1%]). Despite widespread personal use, only 8 physicians (2.6%) recommended LLMs to patients. Notable specialty and demographic variations emerged, with younger physicians showing higher enthusiasm but also elevated legal concerns.

Conclusion

This survey study provides insights into physicians' current usage patterns and perspectives on LLMs. Liability concerns appear to be lessened in settings with institutional LLM access. The findings suggest opportunities for medical centers to consider when developing LLM-related policies and educational programs.

Keywords

cross-sectional survey - large language model - physician - academic medical centers - machine learning

Protection of Human and Animal Subjects

This study was done in adherence to the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and reviewed by the Institutional Review Boards at both participating institutions.


Authors' Contributions

H.J.H., N.H.S., and L.S.L. conceived the study and designed the methodology. H.J.H. wrote the first draft of the manuscript. L.S.L., N.H.S., and M.A.P. provided critical review and substantially contributed to the manuscript.


Data Availability

The de-identified survey data and analysis code used in this study will be made available upon publication by the corresponding author upon reasonable request.


Supplementary Material



Publication History

Received: 07 June 2025

Accepted: 29 October 2025

Accepted Manuscript online:
30 October 2025

Article published online:
14 November 2025

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