Interpretable Clinical Classification with Kolgomorov-Arnold Networks

Abstract: Why should a clinician trust an AI prediction? Despite the increasing accuracy of machine learning methods in medicine, the lack of transparency continues to hinder their adoption in clinical practice. In this work, we explore Kolmogorov-Arnold Networks (KANs) for clinical classification tasks on tabular data. Unlike traditional neural networks, KANs are function-based architectures that offer intrinsic interpretability through transparent, symbolic representations. We introduce Logistic-KAN, a flexible generalization of logistic regression, and Kolmogorov-Arnold Additive Model (KAAM), a simplified additive variant that delivers transparent, symbolic formulas. Unlike black-box models that require post-hoc explainability tools, our models support built-in patient-level insights, intuitive visualizations, and nearest-patient retrieval. Across multiple health datasets, our models match or outperform standard baselines, while remaining fully interpretable. These results position KANs as a promising step toward trustworthy AI that clinicians can understand, audit, and act upon.