Medical Students’ Use of Large Language Models: A National Survey

Abstract

Abstract Background Large language models (LLMs) are increasingly embedded in medical education and clinical care settings, yet limited empirical data describe medical students in Canada’s use and perceptions of these tools. We aimed to characterize student engagement including LLMs used, frequency, purposes, trust, accuracy, perceived impacts, and attitudes toward educational and clinical integration. Methods We conducted a national survey of medical students in Canada distributed between November and December 2025. We summarized responses using descriptive statistics and compared results between students in preclerkship versus clerkship using Fisher’s exact test. 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. Conclusions LLM use among surveyed medical students in Canada was nearly universal and perceived favourably. However, 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.

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