Bibliometric analysis of artificial intelligence applications in cardiovascular imaging: trends, impact, and emerging research areas

Abstract

Background: The application of artificial intelligence (AI) in cardiac imaging has rapidly evolved, offering enhanced accuracy and efficiency in the diagnosis and management of cardiovascular diseases. This bibliometric study aimed to evaluate research trends, impact, and scholarly output in this expanding field. Methods: A systematic search was conducted on 14 August 2024 using the Web of Science Core Collection database. VOSviewer, CiteSpace, and Biblioshiny were utilized for data analysis. Results: was the most prolific journal. Keyword analysis highlighted machine learning, echocardiography, and diagnosis as the most frequently occurring terms. A time trend analysis showed a shift in research focus toward AI applications in cardiac computed tomography (CT) and magnetic resonance imaging (MRI), with recent keywords like ejection fraction, risk, and heart failure reflecting emerging areas of interest. Conclusion: Healthcare providers should consider integrating AI tools into cardiovascular imaging practice, as AI has demonstrated the potential to enhance diagnostic accuracy and improve patient outcomes. This study highlights the rising importance of AI in personalized and predictive cardiovascular care, urging healthcare providers to stay informed about these advancements to enhance clinical decision-making and patient management.

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