Revolutionizing Heart Failure Management With Artificial Intelligence: A Narrative Review of Diagnostic, Prognostic, and Therapeutic Innovations

ABSTRACT Background and Aims Heart failure (HF) remains a major global health burden, affecting over 64 million individuals worldwide. Early detection and optimal management are often limited by subjective interpretation of diagnostic tests and variable clinical expertise. Artificial intelligence (AI) has emerged as a transformative technology that can enhance diagnostic precision, risk stratification, and therapeutic decision‐making. This review aims to synthesize current evidence on clinically validated AI applications across the HF care continuum. Methods A narrative review of recent peer‐reviewed studies was conducted to evaluate AI‐based tools applied in electrocardiography (ECG), echocardiography, cardiac magnetic resonance (CMR), remote monitoring, and smart devices. Emphasis was placed on studies reporting validated performance metrics such as area under the curve (AUC), sensitivity, and specificity, and those integrated into clinical workflows or approved by regulatory bodies. Results AI‐enhanced ECG models have demonstrated high diagnostic accuracy for left‐ventricular systolic dysfunction and diastolic impairment, with AUC values up to 0.92; surpassing traditional risk scores. In cardiac imaging, deep‐learning systems now automate ejection‐fraction and diastolic‐function quantification with precision comparable to expert readers. AI‐driven platforms such as EchoGo and PanEcho enable efficient and consistent image interpretation, while wearables and implantable sensors like HeartLogic and CardioMEMS provide real‐time hemodynamic monitoring and predict decompensation several days before clinical deterioration (sensitivity 70%–88%). Over 40 AI‐based cardiovascular tools have received regulatory clearance, supporting their translational maturity. Conclusion AI technologies are redefining HF care by enabling earlier diagnosis, individualized therapy, and proactive monitoring. However, challenges persist regarding data diversity, model transparency, and clinical integration.