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：可解释性对于可信的医学影像诊断至关重要。然而，现有概念驱动型可解释方法存在关键局限：概念瓶颈模型（CBMs）需在推理时对所有预定义概念进行评分以支持人工干预，这给临床医生带来沉重负担；而基于理据的生成方法常依据类别可区分性选择概念，容易偏离诊断本体。为解决这些问题，我们提出神经符号规则蒸馏（NeRD）框架，该框架能生成高效且与本体接轨的推理链——既充分又无冗余，且无需人工构建诊断规则。在两个皮肤数据集上的实验证明了其出色的诊断性能和可解释性，盲法专家评估验证了NeRD理据的临床合理性。该方法进一步支持了首项以多模态思维链为基础的专家在环诊断研究，实现了高效且有效的概念级干预。