Accepted for/Published in: JMIR Medical Education
Date Submitted: Jan 10, 2025
Date Accepted: Sep 23, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
“Advantages, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: A scoping review”
ABSTRACT
Background:
In the 21st century, rapid information technology and artificial intelligence advancements have profoundly reshaped medical education. As an emerging technology, Generative Artificial Intelligence (GAI) is driving medical education towards enhanced intelligence, personalization, and interactivity. With its vast generative abilities and diverse applications, GAI redefines how educational resources are accessed, teaching methods are implemented, and assessments are conducted.
Objective:
This study reviewed the current applications of GAI in medical education, analyzes its opportunities and challenges, and identifies its strengths and potential issues in educational methods, assessments, and resources, thereby providing a theoretical foundation for future research and practice.
Methods:
This scoping review used PubMed, Web of Science, and Scopus to analyze literature from 2023 to 2024 on GAI in medical education. After screening and systematic analysis, 137 relevant articles were included, followed by a comprehensive quantitative and qualitative synthesis.
Results:
Thematic analysis indicates that GAI's application in medical education mainly embodies the diversification of educational methods, the scientific evaluation of education assessment, and the dynamic optimization of education resources. However, the literature also highlights current limitations and potential future challenges. To address these issues, we suggest developing specialized models and constructing an integrated system based on general large models for specialized model incorporation, improving model adaptability and localization, promoting resource sharing, refining ethical governance, optimizing human-machine collaboration, and building a balanced educational ecosystem for deeper integration between technology and education.
Conclusions:
GAI holds immense potential for transforming medical education, but widespread adoption requires overcoming complex technical and ethical challenges. Grounded in the theory of symbiotic agency, we advocate for an educational ecosystem that fosters human-machine symbiosis, enabling deep integration of technology and humanism and advancing medical education towards greater efficiency and human-centeredness.
Citation
