Artificial Intelligence in Restrictive Cardiomyopathy: Current Diagnostic Applications and Future Directions

Restrictive cardiomyopathy (RCM) poses a significant challenge in diagnosis, is frequently identified in advanced stages, and has limited therapeutic options, which may lead to adverse cardiovascular outcomes. This narrative review examines the application of artificial intelligence (AI) across key diagnostic modalities and delineates priorities for translational advancement. The discussed diagnostic tools include echocardiography, cardiac magnetic resonance (CMR), electrocardiography (ECG), and electronic health records (EHR). A targeted, non-systematic search of PubMed and Scopus was performed to identify studies focused on model development, validation, or diagnostic accuracy concerning RCM and related infiltrative disorders. The findings suggest that AI can enable earlier detection, standardize imaging protocols, and enhance phenotype-driven management of RCM. Nonetheless, several challenges exist, including limited data access, the absence of external validation, variability across imaging devices and locations, and the imperative for transparent, explainable systems. Key priorities for successful implementation encompass establishing multi-center collaborations, detecting and correcting bias, clinician involvement in deployment, and integrating multimodal data, including imaging, signal data, and -omics. If effectively integrated into clinical practice, AI has the potential to redefine the management of RCM from a condition recognized primarily in its later stages to one characterized by early detection, dynamic risk assessment, and personalized treatment strategies.