Key result
AI shows promise for early HF detection and management despite bias and clinical adoption challenges.
Why the study?
A substantial gap remains between artificial intelligence model development and clinical implementation across the heart failure care continuum.
Answers are grounded in this paper and cite their sources.
This review provides a practical stepwise framework to support the safe, scalable, and sustainable implementation of AI in real-world heart failure care.
PURPOSE OF REVIEW: 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. RECENT FINDINGS: 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.
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Li et al. (2026) conducted a review in Heart failure. Artificial intelligence was evaluated. Artificial intelligence applications demonstrate promise for early detection and management of heart failure, though challenges in bias, generalizability, and clinical adoption remain.
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