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已发表论文
心血管疾病中人工智能的研究热点与前景：文献计量分析
Received 15 July 2025
Accepted for publication 18 December 2025
Published 24 December 2025 Volume 2025:18 Pages 8209—8223
DOI https://doi.org/10.2147/JMDH.S553225
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Jacqueline Dunbar-Jacob
Shuhao He, Zihan Shen
College of Information Engineering, Liaoning University of Traditional Chinese Medicine, Shenyang, 110847, People’s Republic of China
Correspondence: Zihan Shen, Email 18004014668@163.com
Objective: To analyze the current status, research hotspots, and trends in the application of artificial intelligence (AI) in cardiovascular disease (CVD) using bibliometric methods, providing a reference for future research.
Methods: A systematic search was conducted in the WoSCC for relevant literature published from database inception to March 5, 2025. VOSviewer v.1.6.20 was used for co-occurrence analysis of institutions (≥ 10 publications) and authors (≥ 5 publications), and Scimago Graphica V1.0.25 was used to visualize collaboration networks among countries/regions. CiteSpace 6.3.R1 was employed for institutional co-occurrence analysis (≥ 5 publications), keyword co-occurrence, and clustering analysis.
Results: A total of 1738 relevant articles were included, with a gradual increase in annual publications, especially after 2018. The United States led in both publication volume and total citations. Harvard Medical School was the most prolific institution. Saba, Luca, and Suri, Jasjit S. were the most productive authors. 《IEEE ACCESS》was the journal with the most publications. High-frequency keywords included machine learning, coronary heart disease, and CVD, forming 10 clusters. Main research areas included AI in disease diagnosis, classification, biomarker discovery, and AI system design. Co-cited literature clusters into four AI-CVD directions: classification, risk prediction, algorithm refinement, imaging. In addition, issues such as the interpretability and clinical acceptance of AI data quality and patient privacy protection models cannot be ignored.
Conclusion: Research on AI in the field of CVD is still in a stage of rapid development. Currently, the hotspots in this field focus on the application of AI in CVD diagnosis and classification, the application of AI in CVD risk prediction, and the precise utilization of AI in CVD imaging. How to develop explainable AI models is a hot topic of research in the coming period.
Keywords: artificial intelligence, cardiovascular disease, citespace, VOSviewer, visualization analysis
College of Information Engineering, Liaoning University of Traditional Chinese Medicine, Shenyang, 110847, People’s Republic of China
Correspondence: Zihan Shen, Email 18004014668@163.com
Objective: To analyze the current status, research hotspots, and trends in the application of artificial intelligence (AI) in cardiovascular disease (CVD) using bibliometric methods, providing a reference for future research.
Methods: A systematic search was conducted in the WoSCC for relevant literature published from database inception to March 5, 2025. VOSviewer v.1.6.20 was used for co-occurrence analysis of institutions (≥ 10 publications) and authors (≥ 5 publications), and Scimago Graphica V1.0.25 was used to visualize collaboration networks among countries/regions. CiteSpace 6.3.R1 was employed for institutional co-occurrence analysis (≥ 5 publications), keyword co-occurrence, and clustering analysis.
Results: A total of 1738 relevant articles were included, with a gradual increase in annual publications, especially after 2018. The United States led in both publication volume and total citations. Harvard Medical School was the most prolific institution. Saba, Luca, and Suri, Jasjit S. were the most productive authors. 《IEEE ACCESS》was the journal with the most publications. High-frequency keywords included machine learning, coronary heart disease, and CVD, forming 10 clusters. Main research areas included AI in disease diagnosis, classification, biomarker discovery, and AI system design. Co-cited literature clusters into four AI-CVD directions: classification, risk prediction, algorithm refinement, imaging. In addition, issues such as the interpretability and clinical acceptance of AI data quality and patient privacy protection models cannot be ignored.
Conclusion: Research on AI in the field of CVD is still in a stage of rapid development. Currently, the hotspots in this field focus on the application of AI in CVD diagnosis and classification, the application of AI in CVD risk prediction, and the precise utilization of AI in CVD imaging. How to develop explainable AI models is a hot topic of research in the coming period.
Keywords: artificial intelligence, cardiovascular disease, citespace, VOSviewer, visualization analysis