Key result
ECG Buddy AI demonstrates high diagnostic discrimination for emergency cardiac endpoints, matching prespecified non-inferiority benchmarks.
Why the study?
The external validity within U.S. healthcare settings of an ECG-based AI model generating ten digital biomarkers for emergency and cardiac assessment has not been established.
Does an ECG-based AI model accurately detect emergency and cardiac conditions compared to prespecified benchmarks and initial troponin T in a U.S. healthcare setting?
Observational (n=151,748)
Yes
Does an ECG-based AI model accurately detect emergency and cardiac conditions compared to prespecified benchmarks and initial troponin T in a U.S. healthcare setting?
Effect estimate: AUC 0.883-0.949
An ECG-based AI model demonstrated high diagnostic accuracy for ten emergency and cardiac conditions in a U.S. cohort, outperforming initial troponin T for detecting AMI and STEMI equivalents.
Reassures on ECG-AI generalizability to US emergency practice; leaves open impact of integration on screening, triage, and outcomes.**[[1]](https://www.nature.
An ECG-based artificial intelligence (AI) model was previously developed to generate ten digital biomarkers for emergency and cardiac assessment and is currently deployed in clinical practice in Korea (ECG Buddy, ARPI Inc.). Its external validity within U.S. healthcare settings has not been established. This study evaluated model performance using a large-scale, multi-center U.S. dataset via the Mayo Clinic Platform (MCP) Discover. Two validation cohorts were assessed: an emergency-diagnosis cohort (mortality, AMI, STEMI and equivalents, hyperkalemia, pulmonary edema) using initial ED ECGs, and a cardiac-function cohort (left and right ventricular systolic dysfunction, pulmonary hypertension, hemodynamically significant pericardial effusion) anchored to echocardiography or right-heart catheterization. The primary objective was non-inferiority of the Area Under the Receiver Operating Characteristic Curve (AUC) relative to prespecified benchmarks. Across ten target conditions, AUCs ranged from 0.883 to 0.949; all met non-inferiority criteria. Performance was consistent across sex and age strata. In a paired subset (N = 1368), ECG-AI outperformed initial troponin T for AMI (AUC 0.920 vs. 0.878) and STEMI equivalents (AUC 0.932 vs. 0.736; all P < 0.001). These findings support the model's potential for ECG-based screening and triage, and provide a foundation for prospective evaluation of calibration, clinical integration, and impact across diverse populations.
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Lee et al. (2026) conducted an observational in Emergency and cardiac conditions (n=151,748). ECG Buddy artificial intelligence model vs. Prespecified benchmark AUCs was evaluated on Area Under the Receiver Operating Characteristic Curve (AUC) for ten digital biomarkers (AUC 0.883-0.949). The ECG Buddy artificial intelligence model demonstrated high discrimination across ten emergency and cardiac-function endpoints with AUCs ranging from 0.883 to 0.949, meeting all prespecified non-inferiority criteria.
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