ArticleCurrent cardiology reviews2026
Artificial Intelligence: A Game Changer in the Diagnosis, Treatment, and Management of Chronic Heart Failure.
Article in Current cardiology reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial intelligence-enabled deep clinical phenotyping in heart failure with preserved ejection fraction: from patient subtypes to precision care.Frontiers in cardiovascular medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionChronic heart failure (CHF) represents a major global health burden. This review explores the potential of artificial intelligence (AI) in improving its diagnosis, treatment, and management.
methodsThis study conducted a comprehensive literature review to evaluate the current and emerging applications of AI in CHF. Databases, such as PubMed, Scopus, and IEEE Xplore, were searched for peer-reviewed articles published between 2015 and 2025, focusing on AIbased diagnostic tools, predictive modeling, treatment personalization, and remote monitoring systems.
resultsSignificant advancements were identified in AI-enhanced diagnostics, predictive models for hospital readmissions, personalized treatment optimization, and AI-driven remote monitoring systems. These technologies have demonstrated improvements in diagnostic accuracy, risk stratification, and real-time patient management. DISCUSSION: AI offers substantial benefits for CHF management by enabling data-driven, individualized care. Nonetheless, challenges remain, including variability in data quality, lack of algorithm transparency, and ethical considerations regarding patient privacy and accountability.
conclusionAI holds transformative potential for CHF management. Its successful integration can enhance diagnostic precision, personalize treatment, and support proactive patient care- ultimately improving outcomes and reducing the global burden of CHF.
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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.