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How AI Is Transforming Medical Education: Bibliometric Analysis.

Youyang Wang1, Chuheng Chang2, Wen Shi3

  • 1Department of General Practice (General Internal Medicine), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) is transforming medical education, shifting towards generative AI. Further research is needed to integrate AI, involving educators and students for future advancements.

Keywords:
AIartificial intelligencebibliometric analysisgenerative AIgenerative artificial intelligencemedical educationresearch trends

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Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Bibliometrics

Background:

  • Artificial intelligence (AI) integration in medical education is accelerating.
  • AI promises advanced tutoring and curriculum reform but faces unpreparedness and community anxiety.
  • Foundational AI in medicine has evolved significantly, with new research emerging rapidly.

Purpose of the Study:

  • To systematically analyze research trends and future directions in AI-enabled medical education.
  • To identify temporal trajectories and key developments in the field using bibliometric analysis.

Main Methods:

  • Systematic bibliometric analysis of publications from Web of Science and Scopus (2000-2024).
  • Utilized information retrieval, cocitation analysis, and citation burst detection.
  • Examined journal and author impact, research trends, and citation lineage via k-means clustering.

Main Results:

  • Publication rates increased significantly post-2021, with foundational work from 2019.
  • Research shifted from computer-assisted learning to generative AI platforms.
  • AI applications are expanding from undergraduate to graduate and continuing medical education, with limited integration between generative AI and traditional AI studies.

Conclusions:

  • Critical need for interdisciplinary studies integrating generative AI with foundational medical AI.
  • Essential to involve medical educators and students in AI development processes.
  • Future research should prioritize theoretical frameworks and collaborative projects for a cohesive knowledge base.