这篇论文展示了KAN不只是理论上可解释，还能落地到医学影像中，让解释更可靠，比标准ResNet/ViT定位准确率提升10%。
研究者提出KANEx框架，首次利用KAN（Kolmogorov-Arnold Networks）的符号透明性来支撑VLM推理，从而生成更可信的文本解释。同时设计了KAN-Map热力图方法，直接从KAN模型导出而非梯度近似。在MIMIC-CXR数据集上，基于KAN的ResNet/ViT架构语义相似性提升，并产生更忠实的显著性图。定位和推理质量比基线提高10%。论文认为将语言解释和视觉归因建立在数学可解释单元上是可信医疗AI的必要步骤。
KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability
Computer vision models have become highly effective for medical applications, yet their black-box nature continues to undermine clinician trust. In clinical workflows, chest X-ray classifiers are increasingly paired with Vision-Language Models (VLMs) to generate natural-language explanations. However, these systems add linguistic fluency without addressing the underlying opacity of the visual model. With the emergence of Kolmogorov-Arnold Networks (KANs), whose spline-based components provide inherently interpretable functional units, we investigate whether this architectural transparency can be leveraged to produce more trustworthy textual explanations. We introduce KANEx, the first ever framework that leverages the symbolic transparency of KANs to ground VLM reasoning. This interpretability also made it possible to design KAN-Map, a novel heatmap generation method derived directly from KAN models rather than gradient approximations. We feed these grounded contexts into downstream VLMs for enhanced explainability. Benchmarked on the MIMIC-CXR dataset, we demonstrate that KAN-based architectures with ResNet/ViT baselines demonstrate improved semantic similarity while producing significantly more faithful saliency maps. KAN architectures improve visual localization and downstream reasoning quality by 10%. Our findings suggest that grounding linguistic explanations and visual attributions in mathematically interpretable units is a necessary step toward trustworthy medical AI.