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July 6, 2026International Journal of Medical InformaticsOpen Access

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

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Authors

ABAustin A BarrRRRobert C RozmanKLKevin Liu

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Overview

National survey quantifies large language model use and perceptions among medical students, indicating a need for structured training.

Key Points

  • This survey aims to assess the prevalence and frequency of large language model use among medical students in Canada, along with their perceptions regarding utility and barriers.
  • National cross-sectional survey of English-speaking medical students conducted from November to December 2025.
  • 286 respondents from 10 Canadian medical schools participated through channels like student unions and organizations.
  • Data collected on frequency of use, perceptions of impact, and barriers to effective LLM integration.
  • 96.50% of respondents reported using at least one large language model, primarily ChatGPT (93.36%).
  • Daily/weekly LLM use was most common for coursework assistance (60.22%) and clinical questions (57.14%).
  • 81.62% observed positive impacts on efficiency, but 90.18% reported encountering inaccurate information.

Cite This Study

Barr et al. (2026) studied this question.

synapsesocial.com/papers/6a4b453d997070ff83b5b0cbhttps://doi.org/10.1016/j.ijmedinf.2026.106589
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Student-Centered Approach Towards Implementing Large Language Models (LLMs) in Medical Education2026 · 1 citations
  2. 2Medical Students' Perspectives on and Usage of Large Language Models in Medical Education: An Exploratory Survey (Preprint)2025
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  5. 5Uptake of Large Language Models by London Medical Students: Exploratory Qualitative Interview Study2026 · 4 citations