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Uptake of Large Language Models by London Medical Students: Exploratory Qualitative Interview Study.

Maya Alazzawi1, Kyle Lam1

  • 1Department of Surgery and Cancer, Imperial College London, London, United Kingdom.

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PubMed
Summary

Medical students frequently use large language models (LLMs) like ChatGPT for learning, but face challenges with accuracy and overreliance. Curricula must adapt to ensure safe and effective integration of AI in medical education.

Keywords:
AIartificial intelligencegenerative artificial intelligencelarge language modelsundergraduate medical education

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Health Professions Education

Background:

  • Large language models (LLMs) are increasingly popular in healthcare.
  • A gap exists in understanding medical students' real-world experiences with LLMs.

Purpose of the Study:

  • To explore perceived benefits, facilitators, and barriers of LLM use among London medical students.

Main Methods:

  • Semistructured interviews with 15 medical students.
  • Inductive thematic analysis guided by the technology acceptance model.

Main Results:

  • Students use LLMs for summarization, concept clarification, and question generation, valuing speed and ease of use.
  • Facilitators include peer learning and accessibility; barriers include inaccuracies, overreliance, and privacy concerns.
  • Junior students struggle to detect inaccurate LLM outputs, impacting trust and critical thinking.

Conclusions:

  • LLMs offer efficiency but raise concerns about reliability, de-skilling, and academic integrity.
  • Urgent need for curricula to guide safe LLM use and adapt teaching/assessment strategies.
  • Future research should include diverse settings and educator perspectives for AI-era curriculum development.