Artificial Intelligence-Enabled Electrocardiogram Screening for Detection of Low Ejection Fraction in Routine Care: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Left ventricular systolic dysfunction (LVSD) is common and treatable but often undiagnosed, especially in early stages with non-specific symptoms. AI applied to standard ECGs offers low-cost screening for low ejection fraction (EF), yet its real-world impact on diagnosis remains unclear.

A systematic review and meta-analysis of RCTs assessed AI-ECG screening for low EF in clinical practice. The protocol was registered with PROSPERO (CRD420261287652). Databases (PubMed, Scopus, Cochrane Central, Google Scholar) were searched from inception to January 18, 2026, for RCTs comparing AI-ECG screening to usual care in adults without known heart failure undergoing routine ECGs. The primary outcome was new low EF (≤ 50%) confirmed by echocardiography. Secondary outcomes included echocardiography referral rates, positive predictive value, and cardiology referral rates. Pooled risk ratios (RR) with 95% CIs were calculated using a random-effects model.

Three RCTs were included (two individual and one cluster) with 37,467 participants. After adjusting for clustering, AI-ECG screening significantly improved detection of new low EF diagnoses compared to usual care (RR 1.36, 95% CI 1.12 to 1.64; p = 0.002; I² = 0%), with a number needed to screen of 204. No significant difference was found in echocardiography referral rates (RR 1.02, 95% CI 0.96 to 1.09; p = 0.55; I² = 36%). The PPV of the AI tool ranged from approximately 20% to 34% across studies.

AI-ECG screening added to routine care improves low EF detection without substantially increasing echocardiography referrals. These findings support AI-ECG for earlier LVSD detection in at-risk populations, though further validation across diverse AI platforms is warranted.