Global research trends in artificial intelligence in cardiovascular medicine: A bibliometric analysis

Cardiovascular diseases significantly impact human health and quality of life. Artificial intelligence has shown great potential in cardiovascular medicine because of its advanced data processing and analytical capabilities. However, a comprehensive bibliometric analysis summarizing global research trends and identifying emerging areas is lacking.

To delineate a comprehensive bibliometric panorama of artificial intelligence applications in cardiovascular medicine over the past decade, and to furnish evidence-based, actionable, decision-making references for researchers, policymakers and clinicians in this domain.

We conducted a bibliometric analysis of publications on artificial intelligence applications in cardiovascular medicine from 2015 to 2024, sourced from the Web of Science Core Collection database. Visualization and analysis were performed using VOSviewer, CiteSpace and the R package "bibliometrix".

A total of 914 articles from 76 countries were analysed, revealing significant growth in research output. The USA and China led in publication volume, Harvard University was the most prolific institution and Frontiers in Cardiovascular Medicine emerged as the leading journal. Johnson et al. (2018), published in the Journal of the American College of Cardiology, was the most cited article, and "prediction" and "classification" were identified as primary research hotspots. Emerging research directions included cardiomyopathy and aortic stenosis, with an emphasis on predictive analytics, disease classification, healthcare modelling and coronary artery disease.

This bibliometric analysis highlights robust growth and global collaboration in artificial intelligence applications within cardiovascular medicine. Current research emphasizes predictive analytics and disease classification, focusing increasingly on cardiomyopathy and aortic stenosis. This study provides insights into current trends, identifies research gaps and suggests potential avenues for future studies, facilitating further innovation and clinical translation in cardiovascular medicine.