Physician Perspectives on Large Language Models in Health Care: A Cross-Sectional Survey Study - Hong, Hyo Jung; Shah, Nigam H.; Pfeffer, Michael A.; Lehmann, Lisa S. (Applied Clinical Informatics, 2025)
This study aims to evaluate physicians' practices and perspectives regarding large language models (LLMs) in health care settings.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.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.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