Artificial intelligence in heart failure — a comprehensive literature review

Heart failure (HF) is a leading cause of hospitalization and mortality worldwide, presenting significant management challenges due to its heterogeneity and frequent comorbidities. Despite advancements in treatment, heart failure poses significant burdens on healthcare, increased by the aging of populations and rising prevalence. Recent developments in artificial intelligence (AI) and machine learning are transforming HF management by improving diagnosis, risk stratification, personalized treatment, and remote monitoring. AI enhances diagnostic accuracy through tools like echocardiogram and electrocardiogram analysis and identifies phenotypic subgroups to better target therapies. AI algorithms integrate data from electronic health records, biomarkers, and wearable devices to predict exacerbations and tailor treatments, while AI-driven Clinical Decision Support Systems (AI-CDSS) improve guideline-directed medical therapy (GDMT) adherence and enable timely interventions. However, barriers such as data integrity, ethical considerations, and insufficient clinician training impede AI adoption. Potential solutions include federated learning to safeguard data privacy and interdisciplinary efforts to establish regulatory frameworks. This review synthesizes current research on AI's applications in HF management, emphasizing its potential to improve patient outcomes, reduce hospitalizations, and address the growing healthcare burden while highlighting the need to confront these challenges to fully realize its benefits.