In this section
@article{Patlatzoglou:2025,
author = {Patlatzoglou, K and Pastika, L and Barker, J and Sieliwonczyk, E and Khattak, GR and Zeidaabadi, B and Ribeiro, AH and Ware, JS and Peters, NS and Ribeiro, ALP and Kramer, DB and Waks, JW and Sau, A and Ng, FS},
journal = {npj Digital Medicine},
title = {The cost of explainability in artificial intelligence-enhanced electrocardiogram models},
year = {2025}
}
TY - JOUR
AB - 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, trust-worthy 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.
AU - Patlatzoglou,K
AU - Pastika,L
AU - Barker,J
AU - Sieliwonczyk,E
AU - Khattak,GR
AU - Zeidaabadi,B
AU - Ribeiro,AH
AU - Ware,JS
AU - Peters,NS
AU - Ribeiro,ALP
AU - Kramer,DB
AU - Waks,JW
AU - Sau,A
AU - Ng,FS
PY - 2025///
SN - 2398-6352
TI - The cost of explainability in artificial intelligence-enhanced electrocardiogram models
T2 - npj Digital Medicine
ER -