Abstract / Summary
Aim: Asymptomatic left ventricular dysfunction (LVD) represents a key and frequently under-recognized stage in the heart failure (HF) continuum. Early identification enables targeted prevention and timely initiation of evidence-based therapies. Artificial intelligence–enabled electrocardiography (AI-ECG) may provide a scalable alternative to imaging for LVD detection. This systematic review synthesized evidence from exclusively externally validated AI-ECG studies evaluating the detection of asymptomatic LVD, with symptomatic HF cohorts included to contextualize the evidence base across the HF continuum. Methods: A systematic literature search was conducted on 8th September 2025. The main output was the pooled area under the receiver operating characteristic curve (AUROC). Results: From 3,587 records, 12 asymptomatic and 30 symptomatic studies were identified (538,735 participants). In asymptomatic cohorts evaluating left ventricular systolic dysfunction (LVSD), AI-ECG demonstrated promising but heterogeneous discriminatory performance (I²>85%) with pooled AUROCs of 0.87 (95% CI 0.75–0.99) for LVEF ≤35%, 0.93 (95% CI 0.88–0.97) for LVEF ≤40%, and 0.89 (95% CI 0.85–0.97) for LVEF ≤50%. Symptomatic cohorts showed AUROCs of 0.89 (95% CI 0.86–0.92), 0.90 (95% CI 0.88–0.92), and 0.85 (95% CI 0.83–0.86) respectively. Asymptomatic left ventricular diastolic dysfunction (LVDD) demonstrated moderate performance (AUROC 0.76, 95% CI 0.66–0.86) with no externally validated symptomatic LVDD studies identified. Small-study effects were detected exclusively in asymptomatic cohorts. Conclusions: Discriminatory performance of AI-ECG in asymptomatic LVSD is broadly comparable to symptomatic populations. High risk of bias driven by prolonged ECG-to-echocardiogram intervals reflecting real-world logistical constraints necessitate cautious interpretation of results and further prospective validation.