Medical Students' Perspectives on and Usage of Large Language Models in Medical Education: An Exploratory Survey (Preprint)

Abstract

BACKGROUND Large Language Models (LLMs) such as ChatGPT are increasingly used by medical students for studying, research, and clinical applications. Despite their growing presence within medical education, concerns persist regarding accuracy, bias, and overreliance. OBJECTIVE To assess how medical students worldwide are using LLMs and to understand their perceptions of and concerns around these tools. METHODS A cross-sectional survey was administered using Qualtrics™ with broad advertising using social media platforms like LinkedIn, Twitter, and Facebook. Responses were summarized using descriptive statistics. RESULTS Among 120 respondents from 13 countries, 88% reported using LLMs, primarily for studying. Common applications included answering questions (67%), summarizing text (51%), and generating practice questions (37%). ChatGPT was the most used LLM (90%). While 84% of medical students reported a positive educational impact, concerns included accuracy (81%), bias (62%), and ethical issues. CONCLUSIONS LLMs are widely used and generally positively perceived by medical students. However, ethical concerns and the need for training in responsible use highlight the importance of educator guidance and further research into educational and clinical outcomes.

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