Physician Use of Large Language Models: A Quantitative Study Based on Large-Scale Query-Level Data.

Background: Generative artificial intelligence (GenAI) has rapidly emerged as a promising tool in health care. Despite its growing adoption, how physicians make use of it in medical practice has not been qualitatively studied. Existing literature has largely focused on theoretical applications or experimental validations, with limited insight into real-world physician engagement with GenAI technologies. Objective: The aim of this study was to leverage a fine-grained dataset at the query level to quantitatively examine how physicians incorporate GenAI into their clinical and research workflows. The primary objective was to analyze usage patterns over time and across physician demographics. A secondary goal was to assess potential risks to patient privacy arising from physicians' interactions with GenAI platforms. Methods: This study collected 106,942 query-and-answer pairs by 989 physicians between August 29, 2023, and April 16, 2024. We performed topic classification to identify the most prevalent use cases, examining how these use cases evolved over time and across demographics. We also developed sensitivity classifiers to detect personally identifiable information in physicians' queries to explore the potential privacy breach risks around physicians' use of GenAI. Results: Approximately 40% (396/989) of the enrolled physicians were female, 45.9% (454/989) were younger than 25 years, and 54.1% (535/989) were between 25 and 56 years of age. The majority of them worked in clinical departments (680/989, 68.8%) or medical technology departments (127/989, 12.8%). Our classification-based quantitative analyses suggest the following. First, physicians use GenAI predominantly for medical research (64,379/106,942, 60.2%) rather than clinical practice (13,100/106,942, 12.25%). Second, physicians focus more on health care-related questions (rising from 64,165/106,942, 60% to 83,415/106,942, 78%) within the first 15% (16,041/106,942) of their query sequence. Third, the use of GenAI differed across physician demographics and features. Specifically, female physicians asked a larger proportion of clinical questions (female: 0.154 vs male: 0.108; P<.001) and administration questions (female: 0.027 vs male: 0.018; P<.001) than male physicians; younger physicians posed more clinical questions (age ≤25: 0.146 vs age ∈ (25, 40]: 0.115 vs age >40: 0.103; P<.001) but fewer research questions (age ≤25: 0.580 vs age ∈ (25, 40]: 0.607 vs age >40: 0.664; P<.001) than senior physicians; and physicians accessing GenAI via computers asked more research questions (computer: 0.637 vs mobile: 0.296; P<.001), whereas physicians using mobile devices asked more clinical questions (computer: 0.107 vs mobile: 0.264; P<.001). Fourth, only 2.68% (2866/106,942) of physician queries contained sensitive information, the majority of which were primarily derived from writing and editing. Conclusions: Physicians are actively integrating GenAI into their professional routines, primarily leveraging it for research but also increasingly for clinical support. Usage patterns vary significantly across demographic lines, including gender, age, and device preference. Despite the presence of sensitive information in some queries, the risk of privacy breaches appears to be low.