The rapid development of Artificial Intelligence (AI) technologies has significantly impacted the diagnosis, treatment, and management of Cardiovascular Disease (CVD), offering new opportunities to address the rising incidence and mortality rates. This study employs bibliometric analysis to explore the research hotspots and trends in AI applications for CVD, utilizing tools such as CiteSpace, VOSviewer, Scimago Graphica, and Microsoft Office Excel 2021. The analysis covers literature from the Web of Science Core Collection (WOSCC) database, focusing on articles and reviews published in English from the database's inception to March 5, 2025. The search strategy includes terms related to AI and CVD, with specific attention to machine learning and deep learning techniques. The study aims to identify research hotspots, assess the advantages and limitations of AI in CVD, and provide insights for future research. The results reveal a growing body of literature, with the number of publications increasing exponentially since 2018. The United States, China, and India lead in terms of publication output and citation frequency. Harvard Medical School is the most prolific institution, with Mayo Clinic and Leland Stanford Junior University following closely. Authors like Saba, Luca, and Suri, Jasjit S. have contributed significantly, focusing on AI applications in CVD imaging. The analysis of keywords and clusters highlights research hotspots such as electrocardiogram-based deep learning, biomarker identification, and AI systems design. Co-citation analysis identifies influential literature and research themes, emphasizing the role of AI in CVD diagnosis, risk prediction, and imaging assessment. The discussion section addresses the current research status, research hotspots, and challenges, including data quality, privacy protection, and model interpretability. The study concludes by emphasizing the potential of AI in CVD management and the need for further research to address challenges and promote standardization and intelligence in AI applications.
