Artificial Intelligence in Heart Failure: From Earlier Disease Detection to Integrated Care Delivery

Artificial intelligence (AI) is increasingly positioned to improve heart failure (HF) care by predicting risk, enabling earlier disease identification, and supporting more reliable delivery of evidence-based management. Current applications span opportunistic detection for low ejection fraction, HF with preserved ejection fraction, and structural heart disease using tools such as AI-enabled electrocardiography, imaging- and electronic health record‒based risk stratification, clinician-facing decision support, remote medication titration, and wearable monitoring platforms. The strongest evidence suggests that AI is most effective when embedded into active clinical workflows that guide targeted testing, treatment optimization, and longitudinal follow-up, rather than functioning as passive alerts alone. This review examines the emerging landscape of AI-enabled HF care delivery from early detection through chronic management, with emphasis on implementation, workflow integration, and the capacity of these tools to improve diagnostic yield and ultimately scalability.