Diagnostic performance of artificial intelligence–enabled electrocardiography for pulmonary hypertension: a systematic review and meta-analysis

Pulmonary hypertension (PH) is a progressive, under-recognized condition with substantial morbidity and mortality. Its non-specific presentation and multimodal diagnostic pathway contribute to delayed diagnosis. Artificial intelligence (AI)-enabled electrocardiography (ECG) may offer a scalable approach to case finding. This PRISMA-DTA systematic review and meta-analysis was registered in PROSPERO (CRD420251241142). MEDLINE, Embase, Web of Science, Scopus, and IEEE Xplore were searched through July 2026, with Google Scholar and citation searching used as supplementary sources. Eligible studies evaluated AI-enabled ECG in adults against right-heart catheterization (RHC)-confirmed PH or echocardiographic PH-related phenotypes. Two reviewers independently screened studies, extracted data, and assessed risk of bias using QUADAS-2. One ECG-only area under the receiver operating characteristic curve (AUROC) per study was pooled using a random-effects model. Seven retrospective studies were included; six contributed to meta-analysis. The pooled AUROC was 0.885 (95% CI 0.855–0.915), with very high heterogeneity (I² = 98.68%; P < 0.0001). The estimate combined heterogeneous RHC, echocardiographic, and clinical-echocardiographic definitions and therefore did not represent accuracy for uniformly RHC-confirmed PH. Four studies performed external validation. Multimodal models outperformed ECG alone in two studies but were excluded from pooling. AI-enabled ECG shows promising discrimination for PH-related targets, but current evidence is retrospective and heterogeneous. It should be considered an investigational case-finding or triage aid rather than a definitive diagnostic stand-alone rule-out test.