ReviewGlobal cardiology science & practice2025
AI-assisted heart failure management: A review of clinical applications, case studies, and future directions.
Review in Global cardiology science & practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Wearable Devices in Cardiovascular Care: A Narrative Review of the Transition Toward Predictive, Preventive, Personalized, and Participatory Medicine.Healthcare (Basel, Switzerland) · 2026Review
- The expanding role of artificial intelligence in personalised medicine: from innovation to individualized care.Frontiers in medicine · 2026Review
- Artificial Intelligence-Guided Neuromodulation in Heart Failure with Preserved and Reduced Ejection Fraction: Mechanisms, Evidence, and Future Directions.Journal of cardiovascular development and disease · 2025Review
- Nurse-Led, Remote Optimisation of Guideline-Directed Medical Therapy in Patients with Heart Failure and Reduced Ejection Fraction Across Australia.Journal of clinical medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart failure is a major global health problem that affects over 64 million people and has significant economic costs. Early diagnosis and effective treatment are crucial, but traditional methods can be limited by the complexity and variability of symptoms. New approaches are needed to improve diagnosis and treatment, such as innovative biomarkers, advanced imaging, and personalized therapy. This study explores the application of artificial intelligence (AI) in heart failure diagnosis. The integration of AI in heart failure care holds transformative potential by enhancing diagnostic accuracy, predicting disease progression, and personalizing treatment plans through sophisticated algorithms and machine learning models. Technologies such as automated image analysis, natural language processing, and wearable devices enable continuous monitoring and timely interventions, improving patient outcomes and reducing hospital readmissions. Despite data privacy and algorithm transparency challenges, AI's ability to process vast datasets and provide real-time insights represents a significant leap forward in heart failure management. This review emphasizes AI's promising applications and future directions in reshaping heart failure care.
Identifiers
What OpenQuestion holds
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.