Research Trends and Emerging Themes in Artificial Intelligence and Cardiology: A Bibliometric Analysis of Highly Cited Publications.
Abstract
Objective
This study aimed to identify and characterize the 100 most-cited publications on artificial intelligence (AI) in cardiology, with particular emphasis on citation impact, major contributors, research themes, and collaboration patterns.
Methods
A bibliometric analysis was conducted using Scopus. Bibliometric characteristics, including citation impact, authors, institutions, countries, funding sponsors, journals, research themes, and collaboration patterns, were analyzed using Microsoft Excel, VOSviewer, and RStudio.
Results
The title-based Scopus search identified 3,694 publications, including 3,124 articles (84.6%) and 570 reviews (15.4%). Publication output increased markedly over time, with 3,304 publications (89.4%) published from 2021 onward and a peak of 819 publications in 2025. Research contributions involved 124 countries, with China (846 publications), the United States (815), and India (702) being the leading contributors. The 100 most-cited publications, published between 2013 and 2025, were distributed across 76 sources, comprising 79 articles and 21 reviews, with a mean of 373 citations per publication. Highly cited publications were concentrated mainly between 2017 and 2022, with 2020 showing the highest output (22 publications), while recent publications demonstrated comparatively high annualized citation rates. Collaboration analysis demonstrated interconnected author, departmental, and country-level research networks, highlighting the international and multidisciplinary nature of highly cited AI-cardiology research. The highly cited literature primarily focused on AI- and ML-based heart disease diagnosis, cardiovascular risk prediction, prognostic modelling, ECG interpretation, and cardiovascular imaging. Predictive modelling, feature selection, classification, and model optimization were prominent methodological themes, alongside emerging applications of explainable AI, precision cardiovascular medicine, and IoT- and edge-based intelligent healthcare systems.
Conclusion
AI research in cardiology has expanded rapidly and evolved into a highly international and multidisciplinary field.