Medical students' use of large language models: a national survey

Abstract

BACKGROUND: Large language models (LLMs) are increasingly embedded in medical education and clinical care settings, yet contemporary Canadian data describing medical students' use and perceptions remain limited. OBJECTIVE: To quantify the prevalence, frequency, and patterns of LLM use among medical students in Canada; to characterize perceptions of utility, accuracy, limitations, and impact; and to describe perceived barriers, challenges, and ethical/privacy concerns. METHODS: We conducted a national, cross-sectional survey distributed to English-speaking medical students between November and December 2025. Recruitment occurred through medical school channels, student unions, and national/regional student organizations. RESULTS: Among 286 respondents from 10 medical schools, 96.50% reported using at least one LLM. The most commonly used LLMs were ChatGPT (93.36%) and OpenEvidence (57.69%). Daily/weekly use was most frequent for coursework assistance (60.22%) and clinical questions (57.14%). Most respondents reported positive impacts on efficiency (81.62%), learning (77.01%), and academic performance (59.49%). Students commonly reported encountering inaccurate information (90.18%). Formal instruction on LLM use was uncommon (10.95%), though 67.67% of students agreed medical schools should integrate formal instruction on LLMs. Only 21.43% of respondents felt adequately educated on data privacy regulations applicable to these tools. CONCLUSION: While LLM use among surveyed medical students in Canada was nearly universal and perceived favorably, students reported exposure to inaccurate outputs and substantial gaps in formal training and privacy literacy. These findings support the development of structured curricular guidance on appropriate application of these tools, including information verification practices and ethical, privacy-aware engagement.