Temporal trends of artificial intelligence in medical education: a global perspective

Abstract

This study aimed to analyze the evolution of artificial intelligence (AI) in medical education through a bibliometric analysis of global scientific publications from 2010 to 2024. We focused on AI applications in medical education, clinical training, and realistic clinical simulations. We conducted a search query in Scopus, PubMed, and Web of Science databases, using predefined terms and filters to retrieve peer-reviewed English-language articles related to medicine. The data were analyzed using R and VOSviewer to assess publication trends and the temporal evolution of author keywords, based on trending topics. We identified 195 publications and 2743 citations. A significant increase in the number of publications and citations was observed starting in 2018, with a peak in 2024. The publications originated from 11 countries, with the United States leading in volume but showing limited international collaboration. Keyword co-citation and trending topic analysis revealed a temporal evolution in the research themes. Earlier studies emphasized curricular integration, whereas recent publications have increasingly explored AI applications in clinical case resolution and specialty-specific training, especially in radiology and surgery. Our findings highlight the growing role of AI in competency-based medical education, with tools supporting clinical decision-making and realistic simulation. However, disparities in research output reflect unequal access to infrastructure and technological resources, particularly in low- and middle-income countries. We conclude that while AI is becoming an important educational tool, its integration into medical curricula requires strategic planning, including faculty development, curriculum redesign, and investment in digital infrastructure.

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