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Updated: Jan 7, 2026
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Puntos Calientes y Perspectivas de la Inteligencia Artificial en Enfermedades Cardiovasculares: Un Análisis
1College of Information Engineering, Liaoning University of Traditional Chinese Medicine, Shenyang, 110847, People's Republic of China.
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.
