Abstract
Background: Cardiovascular disease (CVD) remains a leading cause of global morbidity and mortality. With the increasing burden of CVD globally, there is a pressing need for innovative diagnostic and management solutions. The advent of Artificial Intelligence (AI) and Machine Learning (ML) offers promising avenues for addressing these challenges, with potential applications spanning from cardiac imaging to risk prediction.
Objective: This bibliometric analysis seeks to examine the scientific literature on AI and ML in cardiovascular disease.
Methods: A comprehensive bibliometric analysis was conducted on publications retrieved from PubMed, focusing on the role of AI and ML in CVD research from 2013 to 2023. The study zanalyzed publication growth rates, distribution by countries and journals, citations, funding sources, and keyword co-occurrence.
Results: A total of 895 articles were identified, showing an average annual growth rate of 32.6% in publications. The USA, China, and the UK emerged as leading contributors. The most cited article was "Artificial Intelligence in Precision Cardiovascular Medicine", with 394 citations. The National Institute of Health (NIH) was the top funding institution. Key recurring terms included "Machine learning," "Stroke", "Artificial Intelligence", and "Deep learning'.
Conclusions: Integrating AI and ML in cardiovascular medicine signifies a transformative shift, offering solutions to longstanding challenges in CVD diagnosis and management. The surge in publications over the past decade indicates growing interest and potential in this interdisciplinary field. However, as the technology continues to evolve, addressing its ethical and practical challenges is crucial.
Keywords:
Artificial Intelligence, ML, Cardiovascular Disease, Diagnosis and Management, VosViewer, Bibliometrics, CVD, Precision medicineReferences
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