Research Hotspots and Prospects of Artificial Intelligence in Cardiovascular Disease: A Bibliometric Analysis

Abstract

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: 》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.

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