A narrative review on the use of artificial intelligence in cardiovascular medicine

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide despite substantial advancements in prevention, diagnostics, and therapeutics. The integration of artificial intelligence (AI) and machine learning (ML) is transforming cardiovascular medicine, with applications spanning electrocardiogram (ECG) interpretation, advanced imaging analysis, and risk prediction modelling. Authoritative guidelines, observational studies, systematic reviews, and meta-analyses have highlighted the diagnostic and prognostic potential in various domains, including AI-derived physiological age from ECG, automated plaque quantification in coronary computed tomography angiography (CTCA), and time to event survival prediction models. This review aims to synthesise contemporary evidence on AI in cardiovascular diagnostics, prevention, and rehabilitation, and to outline the methodological, ethical, and translational considerations for safe clinical adoption.