Interpretable Clinical Classification with Kolgomorov-Arnold Networks
This addresses the problem of clinician trust in AI for medical decision-making, representing an incremental advancement by applying KANs to a new domain with interpretability enhancements.
The paper tackled the lack of transparency in AI predictions for clinical practice by exploring Kolmogorov-Arnold Networks (KANs) for classification on tabular data, introducing Logistic-KAN and KAAM variants that match or outperform standard baselines while offering intrinsic interpretability.
Why should a clinician trust an Artificial Intelligence (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. In contrast to traditional neural networks, KANs are function-based architectures that offer intrinsic interpretability through transparent, symbolic representations. We introduce \emph{Logistic-KAN}, a flexible generalization of logistic regression, and \emph{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. We release the code for reproducibility in \codeurl.