Sex-specific validation of an artificial intelligence–enabled electrocardiogram for early triage of acute myocardial infarction

Background Timely recognition of acute myocardial infarction (AMI) remains challenging, particularly in women who more often present with atypical symptoms and less overt electrocardiographic findings. Artificial intelligence–enabled electrocardiogram (AI-ECG) analysis offers automated risk estimation based solely on raw waveform data, but prospective evidence supporting sex-consistent diagnostic performance and safe rule-out capability is limited. Purpose To assess sex-stratified discrimination, rule-out safety, and phenotype robustness of an AI-ECG model for AMI in a prospective, multicenter emergency department cohort. Methods In a prospective validation study conducted across 18 emergency departments, consecutive adults presenting with suspected AMI were enrolled. AI-ECG analyzed the initial 12-lead ECG and generated a continuous probability score using prespecified thresholds for low- and high-risk classification.1 The primary endpoint was adjudicated AMI during index hospitalization. Diagnostic discrimination was evaluated using the area under the receiver operating characteristic curve (AUROC). Rule-out safety at the predefined low-risk threshold was assessed by sensitivity, negative predictive value (NPV), and missed AMI rate. Analyses were performed separately in women and men, with additional evaluation across ST-segment elevation (STEMI) and non–ST-segment elevation myocardial infarction (NSTEMI) phenotypes. Results Among 8,493 patients, 3,186 (37.5%) were women. AMI prevalence was lower in women than men (10.5% vs 23.6%). AI-ECG demonstrated comparable discrimination in women and men (AUROC 0.875 vs 0.871). (Table 1) At the prespecified low-risk cutoff, sensitivity was 98.8% in women and 99.8% in men, with high NPV (99.2% and 99.1%, respectively) and missed AMI rates below 1% in both sexes. Performance remained stable across STEMI and NSTEMI, without systematic attenuation in women. In addition, AI-ECG enabled a larger proportion of women to be classified as low risk at presentation. (Figure 1) Conclusion In a prospective, multicenter emergency department cohort, AI-ECG demonstrated sex-consistent discrimination and preserved rule-out safety for AMI. Stable performance across myocardial infarction phenotypes supports AI-ECG as an early triage adjunct with potential to enhance diagnostic equity during initial evaluation.Figure 1Table 1