Heart Valve Centers in the Era of Data-Driven and AI-Enabled Valve Care

Heart valve disease management has entered a phase of unprecedented clinical and organizational complexity. Beyond anatomical valve lesion severity, outcomes are increasingly influenced by myocardial damage, ventricular remodeling, comorbidities, and extracardiac involvement, which often evolve independently of symptoms. This growing dissociation between valve lesions, myocardial involvement, and clinical presentation challenges traditional symptom- and procedure-centered paradigms and contributes to delayed referral, heterogeneous practice patterns, and variable outcomes, despite major advances in surgical and transcatheter therapies. Concurrently, multimodality imaging, longitudinal data acquisition, and expanding therapeutic options have transformed valve care into a data-rich, lifetime-oriented discipline. In this context, artificial intelligence has emerged as a potential facilitator of automated image analysis, phenotypic characterization, risk stratification, and longitudinal monitoring. However, the clinical value of these technologies critically depends on their integration within structured systems capable of ensuring data quality, expert validation, and accountable decision-making. This article argues that contemporary valve care requires an organizational framework that aligns biological complexity with technological innovation and proposes an integrated model combining Heart Valve Clinics, multidisciplinary Heart Teams, institutional Heart Valve Centers of Reference, and coordinated Valve Networks. Within this framework, artificial intelligence supports, rather than replaces, expert clinical judgment by enhancing consistency, reducing unwarranted variability, and enabling more timely and individualized decision-making across the disease trajectory, with the ultimate aim of improving quality, efficiency, sustainability, and equity of care.