Mapping the research landscape of artificial intelligence in heart failure: a bibliometric analysis.
Authors
Affiliations (11)
Affiliations (11)
- Vali-e-Asr Reproductive Health Research Center, Family Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
- Mission Hospital, Asheville, NC, USA.
- Centinela Hospital Medical Center, Inglewood, CA, USA.
- SIU School of Medicine, Springfield, IL, USA.
- Cooper University Hospital, Camden, NJ, USA.
- Cardiovascular Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
- Department of Medicine, Vision Colleges, Riyadh, Saudi Arabia.
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
- Department of Medicine, Arnot Ogden Medical Center, Elmira, NY, USA.
- School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
Abstract
Heart failure (HF) is a complex syndrome with high morbidity and mortality. Despite advancements in treatment, its management remains a challenge. The objective of this study was to map the scientific landscape of artificial intelligence (AI) applications in HF management through a bibliometric analysis. Data were retrieved from the Web of Science Core Collection. Keywords related to AI and HF were used to identify relevant research articles. Various bibliometric tools, such as Biblioshiny, VOS viewer, and CiteSpace, were used for quantitative trends, collaboration networks, and thematic areas, which were assessed. A total of 1332 studies were included in the final analysis. Publication trends show a sharp increase in AI research related to HF from 2016 onward, with 317 studies published in 2025. The most frequent keywords in the field were <i>heart failure</i> (<i>n</i> = 599), <i>machine learning</i> (<i>n</i> = 516), and <i>AI</i> (<i>n</i> = 225). Other significant keywords included <i>mortality</i> (<i>n</i> = 201), <i>diagnosis</i> (<i>n</i> = 168), and <i>risk</i> (<i>n</i> = 162). Cluster analysis identified major research themes, including Cardiac & Cardiovascular Systems, Computer Science - Interdisciplinary Applications, Computer Science - Artificial Intelligence, Health Care Sciences & Services, Radiology, Nuclear Medicine & Medical Imaging, Cell Biology, Medical Informatics, Endocrinology & Metabolism, and Neurosciences. AI has rapidly become a central tool in HF management, with significant contributions from leading countries and institutions. However, further global collaboration and standardized reporting frameworks are needed to ensure the equitable translation of these technologies into clinical practice.