Digital twins in cardiology and cardiovascular disease management: a review

Cardiovascular diseases remain the leading cause of mortality globally, yet traditional population-based risk stratification and standardised treatment protocols often fail to capture individual patient variability in physiology, disease progression, and therapeutic response. Digital twins, dynamic, patient-specific computational replicas integrating multi-modal clinical data with mechanistic and artificial intelligence models, represent a paradigm shift towards precision cardiovascular medicine. This narrative review synthesises current evidence on cardiac digital twin technologies, evaluates their clinical applications across cardiovascular subspecialties, and outlines translational pathways from research prototypes to routine clinical implementation. We conducted a comprehensive narrative review of peer-reviewed literature, regulatory guidance documents, and industry white papers published between 2023 and 2025. Sources included PubMed, Scopus, regulatory agency websites (FDA, EMA), and cardiovascular society position statements. Key search terms encompassed “digital twins”, “in-silico cardiology”, “virtual heart”, “computational modelling”, and disease-specific applications. Evidence demonstrates feasibility and emerging clinical utility of digital twins across multiple domains: electrophysiology (atrial fibrillation substrate mapping, ventricular arrhythmia risk stratification), device therapy optimisation (cardiac resynchronisation therapy response prediction), heart failure monitoring (decompensation forecasting), and structural interventions (procedural planning). Regulatory frameworks increasingly accommodate in silico evidence for device development, though prospective randomised controlled trials remain limited. Cardiac digital twins are transitioning from research tools to translational applications with tangible clinical potential. Realising widespread benefit requires prospective outcome validation, interoperable infrastructure, credible uncertainty quantification, and governance frameworks ensuring equitable access and ethical deployment.