ArticleHealth science reports2026
Revolutionizing Heart Failure Management With Artificial Intelligence: A Narrative Review of Diagnostic, Prognostic, and Therapeutic Innovations.
Article in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.