ReviewJournal of the American Heart Association2025
Artificial Intelligence in Diagnosis of Heart Failure.
Review in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Mapping the research landscape of artificial intelligence in heart failure: a bibliometric analysis.Annals of medicine and surgery (2012) · 2026Article
- Diagnostic Tests for Stage B Heart Failure.Current cardiology reports · 2026Review
- From Molecules to Multidisciplinary Teams: A Contemporary View of Heart Failure Care.Journal of clinical medicine · 2026Article
- Artificial Intelligence-Derived Electrocardiogram Analysis for Identification of Carbon Monoxide-Induced Cardiomyopathy: A Retrospective Study.Medicina (Kaunas, Lithuania) · 2026Article
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Systematic Review of Artificial Intelligence and Electrocardiography for Cardiovascular Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Diagnostic Accuracy of AI-Assisted Focused Cardiac Ultrasound (FOCUS) in Primary Care.Healthcare (Basel, Switzerland) · 2025Article
- The Contribution of Echocardiography to the Diagnosis and Prognosis Stratification of Diabetic Cardiomyopathy.Diagnostics (Basel, Switzerland) · 2025Review
- One Syndrome, Many Faces: A Unified Perspective on Heart Failure Phenotypes.International journal of molecular sciences · 2025Review
- Advancing Heart Failure Care Through Disease Management Programs: A Comprehensive Framework to Improve Outcomes.Journal of cardiovascular development and disease · 2025Review
- Molecular Diagnostics in Heart Failure: From Biomarkers to Personalized Medicine.Diagnostics (Basel, Switzerland) · 2025Review
- Advanced Diagnosis of Hypertrophic Cardiomyopathy with AI-ECG and Differences Based on Ethnicity and HCM Subtype.Journal of clinical medicine · 2025Article
- Factors Affecting Quality of Life of Caregivers of Patients with Heart Failure.Healthcare (Basel, Switzerland) · 2025Article
- Dynamic alignment of large language models for evidence-grounded heart failure decision support.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart failure (HF) is a complex and varied condition that affects over 50 million people worldwide. Although there have been significant strides in understanding the underlying mechanisms of HF, several challenges persist, particularly in the accurate diagnosis of HF. These challenges include issues related to its classification, the identification of specific phenotypes, and the assessment of disease severity. Artificial intelligence (AI) algorithms have the potential to transform HF care by enhancing clinical decision-making processes, enabling the early detection of patients at risk for subclinical or worsening HF. By integrating and analyzing vast amounts of data with intricate multidimensional interactions, AI algorithms can provide critical insights that help physicians make more timely and informed decisions. In this review, we explore the challenges in current diagnosis of HF, basic AI concepts and common AI algorithms, and latest AI research in HF diagnosis.
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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.