A bibliometric analysis of artificial intelligence applications in heart failure

Heart failure (HF) remains a leading cause of mortality and morbidity worldwide, posing substantial challenges for early and accurate diagnosis as well as personalized therapeutic management. With the rapid evolution of artificial intelligence (AI), substantial progress has been made in the clinical translation and interdisciplinary research of HF. AI offers unprecedented potential to improve the diagnostic accuracy and therapeutic efficacy of HF. However, there is a lack of systematic reviews and analyses in the current research landscape, hotspots, and development trends in this field. This study aimed to employ bibliometric methods to systematically clarify the research status, core research directions, and future prospects of AI applications in HF.

Using the Web of Science Core Collection as the data source, we systematically retrieved literature on AI applications in HF published from 2005 to 2025 and conducted a comprehensive bibliometric analysis via VOSviewer, CiteSpace, and SCImago Graphica.

A total of 4,133 records were retrieved initially, among which 4,110 eligible publications were finally included after strict screening. The annual publication output in this field showed accelerated growth since 2019, reaching 1,067 articles in 2025. The United States (1,508 publications) and China (952 publications) ranked as the top two contributing countries. Frontiers in Cardiovascular Medicine was the most prolific journal, whereas Circulation recorded the highest citation frequency. High-frequency keywords included "heart failure, machine learning, AI, risk, and mortality". The mainstream research hotspots concentrated on machine learning algorithms, medical image analysis, and clinical feature extraction.

Research on AI applications in HF has undergone rapid development and established a comprehensive research framework covering disease diagnosis, prognosis assessment, and mechanism investigations. Future research priorities should incorporate more multicenter data for external validation, as well as promoting the construction and sharing of multicenter, multimodal datasets. These steps will further enhance the clinical applicability and credibility of AI-driven models in HF management.