Bibliometric analysis and knowledge graph visualization of artificial intelligence used in medical education: a systematic review

Abstract

Introduction Artificial intelligence (AI) is penetrating medical education and its research status needs to be reviewed and suitably integrated. This study aimed to analyze the current status, hotspots, and trends of AI in medical education. Methods Data were retrieved from the Web of Science Core Collection (WOSCC), PubMed, China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Scientific Journal Database (VIP) databases. Using CiteSpace, relevant data were extracted to analyze burst citation detection for keywords, clustering, keyword timelines, and keyword emergence. VOSviewer was used to generate visual collaborative network graphs for the keyword timelines. Results This study identified 2437 published in English and 326 Chinese papers. Cluster analysis identified three core themes: technology-driven medical educational innovation, the intelligent transformation of clinical ability training, and the construction of ethical risk and governance systems. In western research, the focus has been on specific technologies, such as deep learning and robotic surgery, with concentrated ethical discussions emerging in 2022. However, Chinese research is driven by the New Medicine policy, which focuses on the macro-integration of AI and the medical education system, with ethical review mechanisms appearing in clusters in 2024. In general, both Chinese and English articles considered the improvement of practical ability using augmented reality and virtual reality technology and the paradigm shift from knowledge transfer to training in higher-order thinking. Conclusion While specific technologies and earlier ethical debates have been prioritized in English research, policy-driven systemic integration and later governance frameworks have been emphasized in Chinese research, both coverage on enhancing practical abilities and shifting educational paradigms. In future research, AI should be further integrated with medical education values and student-centered innovations should be promoted.

Keywords

artificial intelligence / medical education / visualization / research hotspots / comparative knowledge integration framework / knowledge mapping technology

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