AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (4.3 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Evidence Synthesis | Open Access

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

Yu-qing Zhang1,2,3,§Xuan Wang1,3,§Ya-ping He4Hong-guo Rong1,3Yan-yan Meng5( )
Institute for Excellence in Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China
The First Clinical Medical College, Beijing University of Chinese Medicine, Beijing, 100700, China
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, China
Qingdao Traditional Chinese Medicine Hospital, Qingdao Hiser Hospital Affiliated of Qingdao University, Qingdao, 266033, China
Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, China

§These authors contributed equally to this work.

Show Author Information

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.

Electronic Supplementary Material

Download File(s)
9570027_ESM.pdf (112.3 KB)

References

【1】
【1】
 
 
Evidence-Based Chinese Medicine and Technology Assessment
Article number: 9570027

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
Zhang Y-q, Wang X, He Y-p, et al. Bibliometric analysis and knowledge graph visualization of artificial intelligence used in medical education: a systematic review. Evidence-Based Chinese Medicine and Technology Assessment, 2026, 2(1): 9570027. https://doi.org/10.26599/eCMTA.2026.9570027

2302

Views

139

Downloads

0

Crossref

Received: 01 February 2026
Revised: 21 February 2026
Accepted: 28 February 2026
Published: 31 March 2026
© 2026 Beijing University of Chinese Medicine. Production and hosting by Tsinghua University Press.

This is an open access article under the CC-BY license (http://creativecommons.org/licenses/by/4.0/).