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Life sciences · Journal article
Frontiers in Cardiovascular Medicine · August 24, 2026
Well-designed and adequately powered for the question it asks.
AI-enabled electrocardiography achieves high diagnostic accuracy for left ventricular systolic dysfunction and improves screening efficiency and triage across multiple care settings. Pragmatic randomized trials demonstrate practice-level impact, including increased identification of reduced ejection fraction in primary care and improved cardiology consultation yield in inpatient wards. However, benefit remains at the level of improved screening and diagnostic efficiency; prospective trials anchored to clinical outcomes such as heart-failure hospitalization or mortality have not yet been completed.
Narrative review synthesizing pragmatic randomized trials, external validation studies, and population cohorts. Multiple populations across care settings: primary care patients, non-cardiology inpatients, emergency-department patients with dyspnea, and population cohorts for risk discrimination. Intervention: AI-enabled 12-lead electrocardiography (AI-ECG) using supervised models trained on paired ECG–echocardiography data and FDA-cleared software for screening left ventricular systolic dysfunction (LVSD) with ejection fraction ≤40%. Compared with: Usual care and NT-proBNP (in emergency-department subgroup); PREVENT-HF risk model alone (in population cohort). Four geographically diverse U.S. health systems (external validation); primary care and inpatient settings in U.S..
Supervised AI-ECG models show high discrimination for reduced ejection fraction across thresholds and identify individuals at higher risk of subsequent LV dysfunction despite normal baseline echocardiogram External validation of 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 In primary care pragmatic trial, AI-ECG increased the number of new low-ejection-fraction diagnoses and directed echocardiography preferentially to screen-positive patients
No prospective randomized trial of AI-ECG versus usual care on hard clinical outcomes (heart-failure hospitalization, mortality) has yet been published 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
Clinicians should recognize AI-ECG as validated software decision support for screening and triage of left ventricular systolic dysfunction, with particular utility in primary care and resource-constrained settings. Confirmatory imaging remains required, especially given prevalence-dependent positive predictive value in some settings.
Rigorous review of multiple study designs including pragmatic randomized trials and external validation of FDA-cleared device, demonstrating consistent diagnostic accuracy and practice-level impact across diverse settings, though primary outcomes remain surrogate measures rather than hard clinical events.
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Quoted from the source exactly as published.
Clinicians should recognize AI-ECG as validated software decision support for screening and triage of left ventricular systolic dysfunction, with particular utility in primary care and resource-constrained settings. Confirmatory imaging remains required, especially given prevalence-dependent positive predictive value in some settings.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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.
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