JMIR Publications

ABSTRACT

Background:

Incorporating artificial intelligence (AI) into medical education has gained significant attention, with numerous studies highlighting its potential to enhance teaching and learning outcomes. However, it lacks a landscape depicting the comprehensive academic performance of AI in the medical education domain.

Objective:

This study aims to analyze the social patterns, productive contributors, knowledge structure and clusters, concerning AI in medical education since the 21st century.

Methods:

Documents were retrieved from the Web of Science Core Collection database from 2000 to 2023. VOSviewer and Citespace were employed to analyze the academic influences, which were categorized by country, institution, sources, and keywords. The variables analyzed encompassed counts, citations, Hirsh index, impact factor, and collaboration metrics.

Results:

A total of 6,046 papers were included for analysis. The USA and China emerge as primary contributors due to their high productivity and recognition levels. Stanford University, University of London and Mayo Clinic are representative institutions in their respective fields. Plos One, Scientific report, and International journal of computer assisted radiology and surgery ranked as the top three most productive journals. The resulting heatmap highlighted several high-frequency keywords, including artificial intelligence, machine learning, education, and medical education.

Conclusions:

Research on AI and medical education is receiving increased attention over the past two decades. This study provides valuable insights and guidance for researchers who are interested in educational technology, as well as recommendations for pioneering institutions and journal submissions. Along with the rapid growth of AI, medical education is expected to gain much more benefits.