Advancing Cardiology and Cardiovascular Disease Diagnosis and Management with Machine Learning And AI: Progress, Potential and Perspective

Abstract

Cardiovascular disease remains the leading global cause of mortality and disability despite substantial advances in pharmacological and interventional therapies. Conventional clinical risk scores and guideline-based pathways, while valuable, often fail to fully exploit the richness of multimodal data generated across the cardiovascular care continuum. Recent progress in machine learning (ML) and artificial intelligence (AI) promises to transform cardiology by enabling earlier detection of disease, more accurate phenotyping, dynamic risk stratification, and individualized management at scale. In diagnostic cardiology, supervised and deep learning models have achieved or exceeded expert-level performance for tasks such as automated electrocardiogram interpretation, echocardiographic quantification, cardiac magnetic resonance segmentation, and coronary imaging-based plaque characterization. In longitudinal care, ML-based prognostic models that integrate clinical, imaging, biomarker, and wearable data are beginning to outperform traditional scores for predicting arrhythmic events, heart failure decompensation, and ischemic outcomes, while reinforcement and decision-analytic methods are being explored for therapy optimization and resource allocation. At the same time, large language models and foundation models offer new capabilities in workflow orchestration, clinical documentation, and decision support, potentially reshaping how cardiologists interact with information. However, the translation of these technologies into routine cardiovascular practice faces persistent challenges, including data quality and shift, limited external and prospective validation, model opacity, algorithmic bias, and fragmented regulatory and reimbursement frameworks. This paper synthesizes contemporary evidence on ML- and AI-enabled diagnosis and management of cardiovascular disease, critically evaluates their clinical performance and implementation readiness across key application domains, and articulates a forward-looking perspective on trustworthy, equitable, and clinically integrated AI in cardiology. Specific emphasis is placed on design principles for robust model development, transparent evaluation, and continuous monitoring; on the role of explainability, human–AI collaboration, and clinician education; and on the opportunities for AI to catalyze precision cardiology through multimodal learning, digital twins, and real-time, patient-centric decision support.