Despite advances in machine learning-based medical image classifiers, the safety and reliability of these systems remain major concerns in practical settings. Existing auditing approaches mainly rely on unimodal features or metadata-based subgroup analyses, which are limited in interpretability and often fail to capture hidden systematic failures. To address these limitations, we introduce the first automated auditing framework that extends slice discovery methods to multimodal representations specifically for medical applications. Comprehensive experiments were conducted under common failure scenarios using the MIMIC-CXR-JPG dataset, demonstrating the framework's strong capability in both failure discovery and explanation generation. Our results also show that multimodal information generally allows more comprehensive and effective auditing of classifiers, while unimodal variants beyond image-only inputs exhibit strong potential in scenarios where resources are constrained.

翻译：尽管基于机器学习的医学图像分类器取得了进展，但在实际应用中，这些系统的安全性与可靠性仍是主要关切点。现有的审计方法主要依赖于单模态特征或基于元数据的子组分析，这些方法在可解释性方面存在局限，且往往无法捕捉隐藏的系统性故障。为应对这些局限，我们首次引入了一种自动化审计框架，该框架将切片发现方法扩展至专门用于医学应用的多模态表示。我们使用MIMIC-CXR-JPG数据集，在常见故障场景下进行了全面实验，证明了该框架在故障发现与解释生成方面的强大能力。我们的结果还表明，多模态信息通常能实现对分类器更全面、更有效的审计，而超越纯图像输入的单模态变体在资源受限的场景中展现出巨大潜力。