Diagnostic Performance of Artificial Intelligence-Enabled Electrocardiography for Pulmonary Hypertension: A Systematic Review and Meta-analysis

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2026
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Abstract
Abstract Background Pulmonary hypertension (PH) is a progressive, under-recognised 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. Methods This PRISMA-DTA systematic review and meta-analysis was registered in PROSPERO (CRD420251241142). MEDLINE, Embase, Web of Science, Scopus and IEEE Xplore were searched to July 2026, with Google Scholar and citation searching used as supplementary sources. Eligible studies evaluated AI-enabled ECG in adults against 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 AUROC per study was pooled using a random-effects model. Results 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. Conclusions 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 or stand-alone rule-out test.
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Authors Karan Raj Nazareth, Eyad Jamileh, Fahad Muhammad, Omar Farooq, Anisha Sequeira, Alishba Awais, Ibrahim Antoun
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Journal European Heart Journal - Digital Health
Year 2026
DOI
10.1093/ehjdh/ztag143
URL
https://doi.org/10.1093/ehjdh/ztag143
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