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ECG-InterpBench Benchmarks Interpretability of Foundation Models.

Yixuan Duan, Wei Qiu· July 31, 2026 View original

Key takeaways

  • Existing ECG foundation model benchmarks lack interpretability evaluation.
  • ECG-InterpBench systematically assesses interpretability using sparse autoencoders.
  • It measures reconstruction fidelity, clinical feature accessibility, and reproducibility.
  • The benchmark reveals distinct interpretability profiles among different models.

Who benefits

HealthcareMedical DevicesPharmaceuticalsAI/ML DevelopmentResearch

Summary

ECG-InterpBench is a new benchmark designed to systematically evaluate the interpretability of ECG foundation model representations, complementing existing performance-focused benchmarks. It uses sparse autoencoders to assess reconstruction fidelity, clinical accessibility of features, and cross-seed reproducibility across various models and configurations.

Current benchmarks for electrocardiogram (ECG) foundation models primarily focus on predictive performance, offering limited insight into how their internal representations can be understood, clinically interpreted, or consistently reproduced. This gap hinders the development of trustworthy AI in healthcare. Researchers have introduced ECG-InterpBench, a novel benchmark specifically designed to systematically evaluate the interpretability of ECG foundation model representations. It employs sparse autoencoders as standardized measurement instruments, matching their capacity across different models to ensure controlled and fair comparisons. The benchmark assesses multiple dimensions of interpretability, including the fidelity of sparse reconstructions, the accessibility and coverage of 49 clinically meaningful ECG measurements by single features, and the reproducibility of features across different random seeds. The evaluation reveals distinct interpretability profiles among various ECG foundation models, with a matched replication confirming that reconstruction fidelity and clinical accessibility identify different leading models.

Why it matters

For healthcare AI professionals, this benchmark provides a crucial tool to select and develop ECG foundation models that are not only accurate but also interpretable and trustworthy, enabling better clinical integration and regulatory compliance.

How to implement this in your domain

  1. 1Utilize ECG-InterpBench to evaluate the interpretability of existing or new ECG foundation models.
  2. 2Incorporate interpretability metrics from ECG-InterpBench into the model selection process for clinical AI applications.
  3. 3Design new ECG models with interpretability as a primary objective, guided by the benchmark's insights.
  4. 4Collaborate with clinicians to validate the clinical relevance of features identified as accessible by the benchmark.

Original post by Yixuan Duan, Wei Qiu

"arXiv:2607.27404v1 Announce Type: new Abstract: Existing benchmarks for electrocardiogram foundation models primarily evaluate downstream predictive performance, providing limited insight into whether their internal representations can be faithfully decomposed, clinically interpr…"

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