Mapping Key Nodes and Global Trends in AI and Large Language Models for Medical Education: A Bibliometric Study

Artificial intelligence (AI) and large language models (LLMs) are transforming medical education by enhancing teaching and assessment methods. Research output has surged, but key bibliometric trends remain underexplored.

We retrieved 547 publications using the Web of Science Core Collection and conducted bibliometric analysis with CiteSpace and other bibliometric tools to examine publication volume, collaboration networks, citations, keywords, and other important bibliometric indicators.

The United States, the United Kingdom and China lead publication output, with institutions like the University of London, the National University of Singapore and Harvard University at the forefront. JMIR Medical Education is a pivotal journal. Research on ChatGPT and LLMs dominates, with growing focus on nursing education, digital health, medical exams, and medical ethics. Clinical reasoning, undergraduate education, and virtual reality have been identified as underexplored areas of research.

AI and LLMs in medical education constitute a fast-evolving field, with journal calls shaping its bibliometric landscape and advancing the discipline. The field remains in a developmental phase, with subfields yet to be clearly defined. Topics such as nursing education, digital health, medical examinations, and conversational agents are gaining traction. Research on ChatGPT and LLMs holds a central and influential role. Emerging areas of focus include medical ethics, training methodologies, and skills development. Clinical reasoning, undergraduate education, and virtual reality in AI and LLMs for medical education are understudied, offering research opportunities.