Key result
AI shows promise for heart failure detection and phenotyping, but prospective outcome evidence remains limited.
Why the study?
This review was conducted to examine the role of artificial intelligence across the heart failure continuum, including early recognition, phenotyping, risk prediction, monitoring, treatment optimization, and emerging agentic AI applications.
Answers are grounded in this paper and cite their sources.
This review summarizes the applications of artificial intelligence across the heart failure continuum, covering early detection, phenotyping, prognostic modeling, and treatment optimization.
This review examines the role of artificial intelligence (AI) across the heart failure (HF) continuum, with particular emphasis on patient identification and early recognition, AI-enabled electrocardiography and echocardiography, etiologic and phenotypic classification, electronic health record-based staging and risk prediction, advanced HF identification, continuous monitoring, treatment optimization, and emerging applications of agentic AI.
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Jindal et al. (2026) conducted a review in Heart failure. Artificial intelligence across the heart failure care continuum (ECG-based screening, AI-assisted echocardiography, multimodal phenotyping, EHR-based risk prediction, remote monitoring, and agentic AI) vs. Conventional clinical assessment and standard heart failure care was evaluated. Artificial intelligence models integrated across 255 reviewed studies show promise for early heart failure detection, phenotyping, and monitoring, though prospective outcome evidence remains limited.
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