ReviewFrontiers in cardiovascular medicine2026
Artificial intelligence-enabled electrocardiography for assessment of left ventricular systolic dysfunction in the era of foundation models.
Review in Frontiers in cardiovascular medicine, 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
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) applied to the standard 12-lead electrocardiogram (AI-ECG) is being developed as a scalable approach to screen for left ventricular systolic dysfunction (LVSD) and support triage for confirmatory testing. Supervised models trained on paired ECG-echocardiography data show high discrimination for reduced ejection fraction across thresholds and can identify individuals at higher risk of subsequent LV dysfunction despite a normal baseline echocardiogram. External validation of an FDA-cleared ECG-AI device across four geographically diverse U.S. health systems confirmed strong diagnostic accuracy, though signal-format compatibility and quality gating meaningfully affect real-world yield. Two pragmatic randomized trials demonstrate practice-level impact. In primary care, AI-ECG increased the number of new low-ejection-fraction diagnoses and directed echocardiography preferentially to screen-positive patients. In non-cardiology inpatient wards, AI alerts improved diagnostic yield through increased cardiology consultation rather than increased imaging volume. In emergency-department patients with dyspnea, AI-ECG supports a prioritization role with high negative predictive value, outperforming NT-proBNP, but requires confirmatory imaging given prevalence-dependent positive predictive value. In population cohorts, adding AI-ECG signals to PREVENT-HF improves near-term heart-failure risk discrimination and reclassification, though without demonstrated benefit on clinical outcomes such as heart-failure hospitalization or mortality. Foundation models pretrained on large ECG datasets reduce labeled-data requirements and improve transportability, but prospective echocardiography-anchored validation is required before broader deployment. FDA-cleared software is available for left ventricular ejection fraction ≤40% screening from 12-lead ECGs as clinician decision support. This review summarizes performance across thresholds and care settings, outlines threshold selection and calibration, and defines priorities for outcome-oriented trials, equitable deployment, and implementation governance.
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.