ArticleNPJ digital medicine2025
Clinically meaningful interpretability of an AI model for ECG classification.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Risk prediction models for inadvertent intraoperative hypothermia in surgical patients: a systematic review and meta-analysis.International journal of nursing studies advances · 2026Review
- Advances in the Interpretation of the Electrocardiogram by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2026Review
- YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception.Scientific reports · 2026Article
- Artificial intelligence-enabled electrocardiography to triage echocardiography for structural heart disease diagnosis in a low-resource setting.American journal of preventive cardiology · 2026Article
- ChatGPT's Limitations in Athlete ECG Interpretation: Evidence from a Multicenter Diagnostic Study.Journal of cardiovascular development and disease · 2026Article
- Explainable artificial intelligence in electrocardiography: A systematic review.Biomedical signal processing and control · 2026Article
- Towards Feasible Home ECG Monitoring: AI-Driven Detection of Clinically Critical Arrhythmias Using Single-Lead Signals.Bioengineering (Basel, Switzerland) · 2026Article
- Interpretable multimodal zero shot ECG diagnosis via structured clinical knowledge alignment.NPJ cardiovascular health · 2026Article
- Systematic Review of Artificial Intelligence and Electrocardiography for Cardiovascular Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges.Current atherosclerosis reports · 2025Review
- Transparent and Robust Artificial Intelligence-Driven Electrocardiogram Model for Left Ventricular Systolic Dysfunction.Diagnostics (Basel, Switzerland) · 2025Article
- The ethics of data mining in healthcare: challenges, frameworks, and future directions.BioData mining · 2025Review
- Electrocardiographic parameter profiles for differentiating hypertrophic cardiomyopathy stages.Journal of arrhythmia · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the high accuracy of AI-based automated analysis of 12-lead ECG images for classification of cardiac conditions, clinical integration of such tools is hindered by limited interpretability of model recommendations. We aim to demonstrate the feasibility of a generic, clinical resource interpretability tool for AI models analyzing digitized 12-lead ECG images. To this end, we utilized the sensitivity of the Jacobian matrix to compute the gradient of the classifier for each pixel and provide medical relevance interpretability. Our methodology was validated using a dataset consisting of 79,226 labeled scanned ECG images, 11,316 unlabeled and 1807 labeled images obtained via mobile camera in clinical settings. The tool provided interpretability for both morphological and arrhythmogenic conditions, highlighting features in terms understandable to physician. It also emphasized significant signal features indicating the absence of certain cardiac conditions. High correlation was achieved between our method of interpretability and gold standard interpretations of 3 electrophysiologists.
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Registered trials
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