Clinically Aligned Explainable AI Improves Myocardial Infarction Detection Using Multi-Lead Electrocardiography

Myocardial infarction (MI), resulting from coronary artery occlusion and myocardial necrosis, is a leading cause of global cardiovascular mortality, requiring rapid diagnosis and localization to guide timely revascularization and optimize outcomes. While artificial intelligence offers a promising avenue to expedite triage and facilitate expert-level interpretation, its clinical adoption remains limited by the ``black box'' nature of many architectures. This opacity undermines clinician trust and impedes integration into clinical workflows. To address this critical gap, we introduce a clinically interpretable deep learning framework combining diagnostic accuracy with transparent decision-making. Our specialized U-Net encoder-decoder architecture processes the 12-lead electrocardiogram as a unified, spatially structured input to preserve inter-lead dependencies essential for detecting coronary territory-specific patterns. Trained on the PTB-XL dataset of 21,799 annotated ECGs, the model performs binary MI detection and classification of major anatomical subtypes: anterior and inferior MI. To enhance clinical trust and explainability, we employed Gradient-weighted Class Activation Mapping, which explains the model's reasoning to confirm that learned features align with established pathophysiological signs. Comparative analysis reveals that the multi-lead model achieves superior discrimination with AUROC 0.97 to 0.99 compared to single-lead variants, with positive and negative predictive values near or exceeding 90%, critical for effective triage and intervention prioritization. Grad-CAM effectively localizes diagnostically relevant regions, aligning with clinical ECG criteria for anterior MI in precordial leads and inferior MI in limb leads, offering valuable visual cues that support clinical decision-making across diverse practice settings. This interpretable framework enhances trust and facilitates clinician-AI collaboration, paving the way for improved MI management through robust decision support and future prospective validation.