The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies:...

Artificial intelligence (AI) is poised to transform heart failure (HF) care across the clinical continuum, yet a substantial gap remains between model development and implementation. This review aims to summarize key AI-enabled innovations across HF care and provide a practical framework for clinical implementation.

AI applications based on electrocardiography, echocardiography, electronic health records, wearable devices, and large language models have demonstrated promise for early detection, diagnosis, risk stratification, and treatment optimization in HF. The field is shifting from retrospective model development toward prospective evaluation, workflow integration, and health-system deployment, though challenges related to bias, generalizability, interoperability, and clinician adoption persist. Here, we propose a practical stepwise framework to support the safe, scalable, and sustainable implementation of AI in real-world heart failure care.