Key result
AI promises to advance cardiovascular medicine by uncovering disease heterogeneity, integrating data, and enhancing treatments.
Why the study?
Modern cardiology must actively explore the growing influence and potential applications of artificial intelligence in clinical practice and research methodology.
This review highlights the growing applications of AI and machine learning across various cardiovascular domains while emphasizing the need for high-quality standards to overcome technical and ethical barriers.
AI may enhance CV strategies via multimodal integration; leaves open rigorous validation amid technical and ethical barriers.
BACKGROUND: Artificial intelligence (AI) has captured the attention of everyone, including cardiovascular (CV) clinicians and scientists. Moving beyond philosophical debates, modern cardiology cannot overlook AI's growing influence but must actively explore its potential applications in clinical practice and research methodology. METHODS AND RESULTS: AI offers exciting possibilities for advancing CV medicine by uncovering disease heterogeneity, integrating complex multimodal data, and enhancing treatment strategies. In this review, we discuss the innovative applications of AI in cardiac electrophysiology, imaging, angiography, biomarkers, and genomic data, as well as emerging tools like face recognition and speech analysis. Furthermore, we focus on the expanding role of machine learning (ML) in predicting CV risk and outcomes, outlining a roadmap for the implementation of AI in CV care delivery. While the future of AI holds great promise, technical limitations and ethical challenges remain significant barriers to its widespread clinical adoption. CONCLUSIONS: Addressing these issues through the development of high-quality standards and involving key stakeholders will be essential for AI to transform cardiovascular care safely and effectively.
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Scuricini et al. (2025) conducted a review in Cardiovascular disease. Artificial intelligence was evaluated. Artificial intelligence offers exciting possibilities for advancing cardiovascular medicine by uncovering disease heterogeneity, integrating multimodal data, and enhancing treatment strategies.
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