Artificial Intelligence and Atrial Fibrillation: A Bibliometric Analysis

Abstract

Background: Artificial Intelligence (AI) has emerged as a transformative tool in managing atrial fibrillation (AF); however, limited bibliometric studies have systematically analyzed its research trends and thematic evolution. This study addressed these gaps by examining the top 100 most-cited papers on AI and AF using comprehensive bibliometric indicators, including the h-index, g-index, and m-index, to evaluate author impact, citation trends, and productivity. Methods: Bibliometric data were extracted from the Scopus database, given its extensive coverage of high-quality literature. Results: A total of 258 papers were identified, with a notable increase in publications after 2020, reflecting heightened research interest. Among these, the United States led contributions with 89 publications, followed by significant input from institutions such as the Mayo Clinic (33 publications). The most prolific author was P.A. Noteworthy, with 24 publications. Journals like the Journal of Cardiovascular Electrophysiology prominently featured AI and AF research, publishing eight of the top 100 most cited articles. The top 100 most cited papers revealed critical themes, including predictive modeling, automated detection of AF episodes, and risk stratification using AI tools. Conclusion: This bibliometric analysis provides valuable insights into the current state and global disparities of AI applications in AF research.

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