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已发表论文
医学教育中人工智能研究的差异与趋势：中国与国际研究的比较性文献计量分析
Received 11 October 2025
Accepted for publication 13 January 2026
Published 31 January 2026 Volume 2026:17 573537
DOI https://doi.org/10.2147/AMEP.S573537
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Md Anwarul Azim Majumder
Songhua Ma,1,2 Qing Zhou,3 Huiqun Wu4
1Department of Physiology, Medical School of Nantong University, Nantong, People’s Republic of China; 2Nantong University Xining College, Nantong, People’s Republic of China; 3Education and Training Department, Affiliated Hospital of Nantong University, Nantong, People’s Republic of China; 4Department of Medical Informatics, Medical School of Nantong University, Nantong, People’s Republic of China
Correspondence: Songhua Ma, Email songhuama@ntu.edu.cn
Objective: This study aims to explore the application of artificial intelligence in medical education by comparing research hotspots and evolutionary trends between China and the international community, ultimately proposing informed educational practices and policy recommendations.
Methods: Literature was retrieved from the core collections of CNKI and Web of Science for the period 2014– 2024, limited to article and review publications. After applying a unified Boolean search strategy and deduplication, the data were analyzed using CiteSpace 6.4.R1 to examine publication trends, collaboration networks, keyword co-occurrence/clustering/burst detection, and co-citation patterns.
Results: A total of 379 Chinese and 552 English records were included. Publications surged after 2018 and peaked during 2023– 2024. International hotspots centered on machine learning, deep learning, and large language models for simulation-based training and clinical reasoning; Chinese studies focused on “New Medical Sciences”, VR/AR, and medical imaging. The emergence of generative artificial intelligence and multimodal large models has become a new frontier in artificial intelligence research within global medical education from 2023 to 2024.
Conclusion: This study is based on a comparison of two databases to reveal the hotspots and differences in artificial intelligence and medical education research between China and the international research community. It not only compensates for the time lag of existing research, but also proposes three major trends driven by artificial intelligence in the development of medical education (generative AI, personalized learning, immersive experience). A complementary pattern exists between technology-driven and scenario-driven orientations. We recommend integrating AI literacy and ethics into curricula, establishing Generative-AI teaching/assessment guidelines, and building cross-institutional, yearly knowledge-map monitoring for sustainable innovation in medical education.
Keywords: artificial intelligence, AI, medical education, literature visualization, generative AI
1Department of Physiology, Medical School of Nantong University, Nantong, People’s Republic of China; 2Nantong University Xining College, Nantong, People’s Republic of China; 3Education and Training Department, Affiliated Hospital of Nantong University, Nantong, People’s Republic of China; 4Department of Medical Informatics, Medical School of Nantong University, Nantong, People’s Republic of China
Correspondence: Songhua Ma, Email songhuama@ntu.edu.cn
Objective: This study aims to explore the application of artificial intelligence in medical education by comparing research hotspots and evolutionary trends between China and the international community, ultimately proposing informed educational practices and policy recommendations.
Methods: Literature was retrieved from the core collections of CNKI and Web of Science for the period 2014– 2024, limited to article and review publications. After applying a unified Boolean search strategy and deduplication, the data were analyzed using CiteSpace 6.4.R1 to examine publication trends, collaboration networks, keyword co-occurrence/clustering/burst detection, and co-citation patterns.
Results: A total of 379 Chinese and 552 English records were included. Publications surged after 2018 and peaked during 2023– 2024. International hotspots centered on machine learning, deep learning, and large language models for simulation-based training and clinical reasoning; Chinese studies focused on “New Medical Sciences”, VR/AR, and medical imaging. The emergence of generative artificial intelligence and multimodal large models has become a new frontier in artificial intelligence research within global medical education from 2023 to 2024.
Conclusion: This study is based on a comparison of two databases to reveal the hotspots and differences in artificial intelligence and medical education research between China and the international research community. It not only compensates for the time lag of existing research, but also proposes three major trends driven by artificial intelligence in the development of medical education (generative AI, personalized learning, immersive experience). A complementary pattern exists between technology-driven and scenario-driven orientations. We recommend integrating AI literacy and ethics into curricula, establishing Generative-AI teaching/assessment guidelines, and building cross-institutional, yearly knowledge-map monitoring for sustainable innovation in medical education.
Keywords: artificial intelligence, AI, medical education, literature visualization, generative AI