Interpretability is essential for trustworthy medical image diagnosis. However, existing concept-driven interpretable methods have key limitations: Concept Bottleneck Models (CBMs) require scoring all predefined concepts at inference time and for manual intervention, imposing a substantial burden on clinicians, while rationale-based generative approaches often select concepts by class discriminability, which can drift from diagnostic ontologies. To address these issues, we propose Neuro-Symbolic Rule Distillation (NeRD), a framework that produces efficient, ontology-grounded reasoning chains that are sufficient yet non-redundant, without manually crafting diagnostic rules. Experiments on two skin datasets demonstrate strong diagnostic performance and interpretability, and blinded expert evaluation confirms the clinical plausibility of NeRD rationales. Our method further enables a first expert-in-the-loop study for Multimodal Chain-of-Thought-based diagnosis, achieving efficient and effective concept-level intervention.

翻译：摘要：可解释性对于可信的医学图像诊断至关重要。然而，现有概念驱动的可解释方法存在关键局限性：概念瓶颈模型（CBM）需要在推理时对所有预定义概念进行评分并支持人工干预，这给临床医生带来了沉重负担；而基于解释的生成方法通常通过类别判别性来选择概念，可能导致其偏离诊断本体。为解决这些问题，我们提出神经符号规则蒸馏（NeRD）框架，该框架无需人工构建诊断规则即可生成高效、锚定本体的推理链，这些推理链既充分又无冗余。在两个皮肤数据集上的实验表明，该方法具有强大的诊断性能和可解释性，且盲法专家评估证实了NeRD诊断解释的临床合理性。我们的方法进一步实现了首个基于多模态思维链诊断的专家介入研究，实现了高效且有效的概念级干预。