Purpose of review The diagnostic evaluation of mitral regurgitation (MR) is complex, time-intensive, and prone to significant interobserver variability. This review examines the current evidence on artificial intelligence (AI) applications across the full MR diagnostic pathway, from pre-imaging screening to advanced multimodality imaging, and explores future directions for clinical integration. Recent findings AI-enabled digital stethoscopes and deep learning-based electrocardiographic models provide scalable upstream strategies for early detection and population-level risk stratification, although they currently function as enrichment tools rather than standalone diagnostics. In echocardiography, AI has demonstrated strong performance for automated valve segmentation, Doppler analysis, severity grading, and phenotypic classification. In cardiac magnetic resonance, AI enables automated valve tracking, ventricular segmentation, and tissue characterization, although dedicated algorithms for direct MR quantification remain under development. Beyond automation, AI-driven approaches have identified clinically meaningful phenotypes linking valvular dysfunction and cardiac remodeling with myocardial fibrosis and increased cardiovascular risk. Summary AI holds significant promise to improve reproducibility, consistency, and clinical integration of MR assessment. Widespread implementation, however, requires prospective validation, standardized acquisition protocols, improved model interpretability and, most importantly, proper regulations driven by a culture of safety.

As most PVs are innately and inherently stenotic, the effective orifice area (EOA) of a PV is frequently small in relation to the patient’s body size, an important phenomenon known as prosthesis/patient mismatch (PPM). In aortic valves, PPM is defined1 as moderate when the indexed EOA is ≤ 0.85 cm/m and is defined as severe when the indexed EOA is ≤ 0.65 cm/ m. In the mitral valve, the cutoff points are 1.2 and 0.9 cm/m, correspondingly. Importantly, PPM has been linked to suboptimal symptomatic improvement, weakened exercise capacity, pulmonary artery hypertension, incomplete regression of left ventricular hypertrophy, increased heart events, and even mortality rates after valve replacement (2-4) According to the literature, PPM is the most common cause of an increased transprosthetic gradient; however, it is essential to distinguish this state from other acquired PV stenoses, which may result from significant leaflet calcification on bioprosthetic valves and pannus overgrowth or thrombus creation on mechanical PVs.