Real-world insights from an AI-ECG decision support platform: AI vs. blinded HCP STEMI ECG interpretation

Abstract Background Early and accurate identification of ST-elevation myocardial infarction (STEMI) is critical for timely reperfusion therapy and improved patient outcomes. However, the accuracy of STEMI detection via ECG interpretation varies among healthcare professionals (HCPs). Artificial intelligence (AI)-assisted ECG interpretation has the potential to reduce this variability and enhance diagnostic accuracy, particularly in real-world settings with diverse provider expertise. Purpose To compare the diagnostic performance of AI-assisted ECG analysis and self-reported HCP interpretations in STEMI detection using a real-world dataset from an AI-integrated ECG analysis platform. Methods A total of 4,598 consecutive 12-lead ECGs were uploaded to an AI ECG platform between June 2023 and January 2024 by 1,423 users for AI decision support. Two expert physicians provided the reference standard, classifying 1,527 (33%) ECGs as STEMI. The AI ECG Model was evaluated on the full dataset. For 1,423 (30%) ECGs, 356 HCPs provided a self-declared initial interpretation, blinded to the AI output. These HCPs included 65 (18%) cardiologists, 199 (56%) non-cardiologist physicians, and 92 (26%) non-physician HCPs. Diagnostic performance for STEMI detection was assessed using sensitivity, specificity, PPV, NPV, and F1 score, with 95% confidence intervals calculated using the Wilson Score Interval and statistical significance determined via the Chi-Square Test. Results The AI ECG Model significantly outperformed HCPs across all evaluated metrics (p 0.001). When tested on 4,598 ECGs, AI achieved 93.5% sensitivity, 87.0% specificity, 78.0% PPV, 96.4% NPV, and an F1 score of 85.1%. In contrast, HCPs across all training levels had 84.6% sensitivity, 73.2% specificity, 55.8% PPV, 92.3% NPV, and an F1 score of 67.2%. Performance comparison by HCP role showed that cardiologists had significantly higher PPV (66.9%) than non-cardiologist physicians (56.7%) and non-physicians (52.0%) (p = 0.012), though sensitivity, specificity, and NPV did not differ significantly. AI remained superior to all subgroups (p 0.05). Conclusion The AI ECG Model demonstrated significantly higher diagnostic accuracy than healthcare professionals overall, including cardiologists. AI-assisted ECG analysis may improve early STEMI detection, reduce misdiagnoses, and support clinical decision-making. Further research is warranted to evaluate its impact on time to reperfusion and patient outcomes.Figure 1