โก Quick Summary
This study explores the trade-off between performance and explainability in artificial intelligence-enhanced electrocardiogram (AI-ECG) models. By introducing a novel framework called VAE-SCAN, the researchers demonstrate how explicit representation learning can enhance ECG interpretability while examining its impact on diagnostic performance.
๐ Key Details
- ๐ Framework: VAE-SCAN for bi-directional associations
- ๐งฉ Technology: Variational Autoencoders (VAE)
- โ๏ธ Focus: ECG feature interpretability and clinical factors
- ๐ Performance: Investigated across models with varying explainability
๐ Key Takeaways
- ๐ค AI-ECG models show exceptional performance in diagnostics but lack transparency.
- ๐ก Explainable AI is increasingly demanded in medical applications for trustworthy decision-making.
- ๐ VAE-SCAN offers a new approach to model ECG features explicitly.
- โ๏ธ Trade-off between performance and interpretability remains a critical area of exploration.
- ๐ Findings indicate that intrinsic ECG interpretability may introduce costs in performance.
- ๐ Potential implications for clinical adoption of AI-ECG models are significant.
- ๐ง Study authors include experts from various institutions, enhancing credibility.
- ๐ Published in: NPJ Digital Medicine, 2025.
๐ Background
The integration of artificial intelligence in healthcare, particularly in diagnostic tools like electrocardiograms (ECGs), has shown remarkable promise. However, the black-box nature of many AI models poses challenges for clinical adoption, as healthcare professionals require transparency and trust in the decision-making process. This study addresses the growing need for explainable AI in medicine, focusing on how to make AI-ECG models more interpretable without sacrificing performance.
๐๏ธ Study
The researchers developed a novel framework, VAE-SCAN, which utilizes variational autoencoders to create explicit representations of ECG features. This study investigates the relationship between these features and clinical factors, aiming to enhance the interpretability of AI-ECG models. The research also examines how different representations affect ECG decoding performance across models with varying levels of explainability.
๐ Results
The findings reveal a significant cost associated with intrinsic ECG interpretability. While the VAE-SCAN framework enhances the interpretability of ECG features, it also highlights the potential trade-offs in performance. This nuanced understanding of the performance-explainability relationship is crucial for future developments in AI-ECG models.
๐ Impact and Implications
The implications of this study are profound for the future of AI in healthcare. By improving the interpretability of AI-ECG models, we can foster greater trust among clinicians and patients alike. This could lead to wider adoption of AI technologies in clinical settings, ultimately enhancing patient care and outcomes. The study encourages further exploration into balancing performance and explainability in AI applications across various medical domains.
๐ฎ Conclusion
This research underscores the importance of explainability in AI-enhanced ECG models. As we strive for more transparent and trustworthy AI systems in healthcare, understanding the trade-offs between performance and interpretability will be essential. The future of AI in medicine looks promising, and continued research in this area is vital for advancing clinical practice.
๐ฌ Your comments
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The cost of explainability in artificial intelligence-enhanced electrocardiogram models.
Abstract
Artificial intelligence-enhanced electrocardiogram (AI-ECG) models have shown outstanding performance in diagnostic and prognostic tasks, yet their black-box nature hampers clinical adoption. Meanwhile, a growing demand for explainable AI in medicine underscores the need for transparent, trustworthy decision-making. Moving beyond post-hoc explainability techniques that have shown unreliable results, we focus on explicit representation learning using variational autoencoders (VAE) to capture inherently interpretable ECG features. While VAEs have demonstrated potential for ECG interpretability, the presumed performance-explainability trade-off remains underexplored, with many studies relying on complex, non-linear methods that obscure the morphological information of the features. In this work, we present a novel framework (VAE-SCAN) to model bi-directional, interpretable associations between ECG features and clinical factors. We also investigate how different representations affect ECG decoding performance across models with varying levels of explainability. Our findings demonstrate the cost introduced by intrinsic ECG interpretability, based on which we discuss potential implications and directions.
Author: [‘Patlatzoglou K’, ‘Pastika L’, ‘Barker J’, ‘Sieliwonczyk E’, ‘Khattak GR’, ‘Zeidaabadi B’, ‘Ribeiro AH’, ‘Ware JS’, ‘Peters NS’, ‘Ribeiro ALP’, ‘Kramer DB’, ‘Waks JW’, ‘Sau A’, ‘Ng FS’]
Journal: NPJ Digit Med
Citation: Patlatzoglou K, et al. The cost of explainability in artificial intelligence-enhanced electrocardiogram models. The cost of explainability in artificial intelligence-enhanced electrocardiogram models. 2025; 8:747. doi: 10.1038/s41746-025-02122-y