Explainable AI assistance significantly improved medical students' overall diagnostic accuracy for acute myocardial infarction from 43% to 82% (p < 0.05).
Does an explainable deep learning framework improve diagnostic accuracy for acute myocardial infarction on 12-lead ECGs in medical students?
An explainable deep learning model for ECG analysis achieves high diagnostic precision for acute myocardial infarction and significantly improves the diagnostic accuracy of novice clinicians.
Absolute Event Rate: 82% vs 43%
p-value: p=< 0.05
Artificial intelligence (AI) holds significant promise for electrocardiogram (ECG) analysis, yet accurately detecting non-ST-segment elevation myocardial infarction (NSTEMI) and overcoming the “black box” nature of deep learning models remain persistent challenges. Here, we present a comprehensive deep learning framework capable of classifying STEMI, NSTEMI, and non-acute coronary syndrome (non-ACS) from 12-lead ECG images, while also localizing infarction sites. Utilizing ,2070 validated ECGs, our pipeline integrates ResNet for acute myocardial infarction detection, Faster R-CNN for ST-segment elevation localization, and an ensemble approach for final classification. The model achieved a 98.3% AUROC for detection and an overall three-class accuracy of 93.6%, with high F1 scores for identifying infarction territories. To address interpretability, we developed an explainable AI (XAI) web viewer that visualizes detected regions. Furthermore, we evaluated the model’s utility as an educational tool in a prospective pilot study with medical students. AI assistance significantly improved the students’ overall diagnostic accuracy from 43% to 82% ( p < 0.05), with notable gains in identifying NSTEMI and complex STEMI subtypes. These findings demonstrate that our interpretable AI model not only supports clinical decision-making with high diagnostic precision but also serves as an effective educational aid for enhancing novice clinicians’ proficiency.
Recent publication with AI cardiology buzz spillover from Nature paper; discussions on X and cardiology forums.
Kim et al. (Sat,) conducted a other in Acute myocardial infarction (STEMI, NSTEMI, non-ACS) (n=2,070). Explainable AI (XAI) assistance vs. Unassisted diagnosis was evaluated on Overall diagnostic accuracy of medical students (p=< 0.05). Explainable AI assistance significantly improved medical students' overall diagnostic accuracy for acute myocardial infarction from 43% to 82% (p < 0.05).