Research landscape thematic evolution and future trends of artificial intelligence in medical education from 2015 to 2025

Abstract

Artificial Intelligence (AI) in medical education is of critical significance for promoting the reform of medical education. The integration of AI technology and medical education has shifted from initial conceptual exploration to technological implementation and educational practice, which helps reduce teachers` workload and improve students` learning efficiency. This study followed the PRISMA guidelines and employed CiteSpace, VOSviewer, and R Bibliometrix to conduct a bibliometric analysis of core literature from the past decade in the Web of Science database. The findings indicated that artificial intelligence medical education research is undergoing rapid development, with core publications emerging in journals such as BMC Medical Education, Journal of Medical Internet Research, and Medical Teacher. Key authors in this field include Friedman, Noseworthy, and Cheungpasitporn, while the United States and China stand as the primary contributing nations. Furthermore, most core research institutions in this domain are located in these regions. This study summarized research hotspots in AI in Medical Education, focusing on three major themes: educational scenarios and core participants, core AI technologies, and educational programs. This has led to the development of a process framework based on scenarios, driven by technology, and implemented through projects. Findings indicate that research in this field has progressed from initial feasibility validation to a stage of deep integration, involving machine learning, deep learning, and large language models. Future research will concentrate on AI in Medical Education, prioritizing three key directions: algorithm optimization, disease diagnosis, and teaching management. This study provided a theoretical and reference foundation for researchers, medical educators, and policymakers in the field of AI in Medical Education.