Artificial Intelligence in Cardiomyopathy: Diagnosis and Personalized Care

Abstract

Background: Cardiomyopathies are a broad category of myocardial diseases and they belong to the most complex problems of cardiovascular medicine due to the clinical presentation, unexpected course of disease and varied response to therapy. Traditional diagnostic and prognostic models are important but limited in sensitivity and focused on linear connections. Objective: This review was undertaken to examine the role of artificial intelligence (AI), from machine learning (ML) to deep learning (DL) and multi-modal integration, to improve the accuracy of cardiomyopathy diagnosis and personalized treatment. Methodology: A structured literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar for articles published between 2014 and 2025, using combinations of the keywords “artificial intelligence,” “machine learning,” “deep learning,” “cardiomyopathy,” “precision cardiology,” “cardiac imaging,” and “electrocardiography.” Original research articles, systematic reviews, and narrative reviews describing the application of AI in the diagnosis, risk stratification, multi-omics characterization, and therapeutic guidance of cardiomyopathies were screened for relevance and eligibility, and the extracted evidence was synthesized narratively under the domains of imaging, electrocardiography, genomics/multi-omics, wearables, and electronic health records. Key Findings: AI is increasing the understanding of disease heterogeneity, from imaging applications, where it enables automated measurement of chambers and identification of fibrosis with unprecedented consistency, to genomics and multi-omics, where it improves the interpretation of variants and polygenetic risk prediction. Also, integration of wearables, ECG and EHR systems further enhance the potential of artificial intelligence (AI) technologies for massive real-time monitoring and prediction of dangers. In addition to the current practices, new directions include explainable AI for trust of practitioners, digital twins for prediction of the illness course and federated learning for equity and worldwide applicability. There are certain regulatory milestones (FDA/EMA approval of some tools) that are indicative of the translational readiness of a product, but issues over algorithmic biases and generalizability persist. Conclusion: In this paper we use existing information to present the blueprint for the future of precision cardiology when the approach to care will become more proactive instead of reactive to improve care for cardiomyopathy all across the world.