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<p>Generative Counterfactual Explainable Artificial Intelligence (XAI) offers a novel approach to understanding how AI models interpret electrocardiograms (ECGs). Traditional explanation methods focus on highlighting important ECG segments but often fail to clarify why these segments matter or how their alteration affects model predictions. In contrast, the proposed framework explores “what-if” scenarios, generating counterfactual ECGs that increase or decrease a model’s predictive values. This approach has the potential to increase clinicians’ trust specific changes—such as increased T wave amplitude or PR interval prolongation—influence the model’s decisions. Through a series of validation experiments, the framework demonstrates its ability to produce counterfactual ECGs that closely align with established clinical knowledge, including characteristic alterations associated with potassium imbalances and atrial fibrillation. By clearly visualizing how incremental modifications in ECG morphology and rhythm affect artificial intelligence-applied ECG (AI-ECG) predictions, this generative counterfactual method moves beyond static attribution maps and has the potential to increase clinicians’ trust in AI-ECG systems. As a result, this approach offers a promising path toward enhancing the explainability and clinical reliability of AI-based tools for cardiovascular diagnostics.</p>

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A novel XAI framework for explainable AI-ECG using generative counterfactual XAI (GCX)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月2日

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  • Jong-Hwan Jang,
  • Yong-Yeon Jo,
  • Sora Kang,
  • Jeong Min Son,
  • Hak Seung Lee,
  • Joon-myoung Kwon,
  • Min Sung Lee

摘要

Generative Counterfactual Explainable Artificial Intelligence (XAI) offers a novel approach to understanding how AI models interpret electrocardiograms (ECGs). Traditional explanation methods focus on highlighting important ECG segments but often fail to clarify why these segments matter or how their alteration affects model predictions. In contrast, the proposed framework explores “what-if” scenarios, generating counterfactual ECGs that increase or decrease a model’s predictive values. This approach has the potential to increase clinicians’ trust specific changes—such as increased T wave amplitude or PR interval prolongation—influence the model’s decisions. Through a series of validation experiments, the framework demonstrates its ability to produce counterfactual ECGs that closely align with established clinical knowledge, including characteristic alterations associated with potassium imbalances and atrial fibrillation. By clearly visualizing how incremental modifications in ECG morphology and rhythm affect artificial intelligence-applied ECG (AI-ECG) predictions, this generative counterfactual method moves beyond static attribution maps and has the potential to increase clinicians’ trust in AI-ECG systems. As a result, this approach offers a promising path toward enhancing the explainability and clinical reliability of AI-based tools for cardiovascular diagnostics.