The Role of Artificial Intelligence in Cardiac Rehabilitation of Patients with Heart Failure

Heart failure remains a leading cause of morbidity and mortality worldwide. Although cardiac rehabilitation is strongly recommended, its implementation is limited by restricted access, heterogeneous patient profiles, and poor long-term adherence. Artificial intelligence (AI) has emerged as a potential tool to enhance personalization and scalability of rehabilitation programs. A narrative review was conducted using PubMed, Scopus, and Web of Science to identify studies addressing AI applications in heart failure and cardiac rehabilitation. Clinical trials, observational studies, guidelines, and methodological papers were selected based on relevance and scientific rigor. AI techniques—including machine learning, deep learning, reinforcement learning, and natural language processing—are being applied across all phases of cardiac rehabilitation. Applications include risk stratification, individualized exercise prescription, real-time monitoring through wearable devices, anomaly detection, behavioral support, and long-term outcome prediction. Early evidence suggests improved personalization, safety, and patient engagement; however, large-scale outcome data remain limited. Despite promising results, challenges related to data quality, algorithm transparency, bias, clinical integration, and regulatory oversight persist. AI has the potential to transform cardiac rehabilitation in heart failure by enabling adaptive, data-driven, and patient-centered care, provided rigorous validation and ethical implementation are ensured.