KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability

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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 in

Source: ArXiv AI