ReviewESC heart failure2025
Use of artificial intelligence for detecting left ventricular dysfunction and predicting incident heart failure risk.
Review in ESC heart failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Diagnostic Tests for Stage B Heart Failure.Current cardiology reports · 2026Review
- Use of artificial intelligence for detecting left ventricular dysfunction and predicting incident heart failure risk.ESC heart failure · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Effective medications are available for the prevention of heart failure (HF). While their use is indicated in patients with risk factors, engagement and adherence among 'at risk' individuals is challenging, as it is with atherosclerotic heart disease prevention. The detection of patients with subclinical cardiac dysfunction could provide a subgroup at heightened risk, warranting more intensive disease management programmes. The process of screening the aging population is a huge task that could be facilitated using artificial intelligence (AI) to identify clinical risk, select 'at risk' individuals by using AI to enhance the value of electocardiography, and facilitate the non-expert acquisition and interpretation of echocardiography. This review, informed by a search of the recent literature, explored how such an AI-informed pathway could permit HF screening to occur in the community-maximizing access and minimizing cost.
Indexed as
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.