The increasing use of machine learning in clinical decision support has been limited by the lack of transparency of many high-performing models. In clinical settings, predictions must be interpretable, auditable, and actionable. This study investigates Kolmogorov-Arnold Networks (KANs) as intrinsically interpretable alternatives to conventional black-box models for clinical classification of tabular health data, aiming to balance predictive performance with clinically meaningful transparency. We introduce two KAN-based models: the Logistic KAN, a flexible generalization of logistic regression, and the Kolmogorov-Arnold Additive Model (KAAM), an additive variant that yields transparent symbolic representations through feature-wise decomposability. Both models are evaluated on multiple public clinical datasets and compared with standard linear, tree-based, and neural baselines. Across all datasets, the proposed models achieve predictive performance comparable to or exceeding that of commonly used baselines while remaining fully interpretable. Logistic-KAN obtains the highest overall ranking across evaluation metrics, with a mean reciprocal rank of 0.76, indicating consistently strong performance across tasks. KAAM provides competitive accuracy while offering enhanced transparency through feature-wise decomposability, patient-level visualizations, and nearest-patient retrieval, enabling direct inspection of individual predictions. KAN-based models provide a practical and trustworthy alternative to black-box models for clinical classification, offering a strong balance between predictive performance and interpretability for clinical decision support. By enabling transparent, patient-level reasoning and clinically actionable insights, the proposed models represent a promising step toward trustworthy AI in healthcare (code: https://github.com/Patricia-A-Apellaniz/classification_with_kans).

翻译：机器学习在临床决策支持中的应用日益广泛，但许多高性能模型缺乏透明度，这限制了其发展。在临床场景中，预测结果必须可解释、可审计且可操作。本研究探索将Kolmogorov-Arnold网络作为传统黑盒模型的内在可解释替代方案，用于表格化健康数据的临床分类，旨在平衡预测性能与具有临床意义的透明度。我们提出两种基于KAN的模型：Logistic KAN（逻辑回归的灵活泛化形式）和Kolmogorov-Arnold加性模型（KAAM，一种通过特征级可分解性实现透明符号表示的加性变体）。两种模型在多个公开临床数据集上进行了评估，并与标准线性模型、基于树的模型及神经网络基线进行对比。在所有数据集中，所提模型在保持完全可解释性的同时，达到了与常用基线相当或更优的预测性能。Logistic-KAN在所有评估指标中获得最高综合排名，平均倒数排名为0.76，表明其在各任务中表现稳定。KAAM在保持竞争性准确率的同时，通过特征级可分解性、患者级可视化及最近邻检索增强透明度，支持对个体预测结果进行直接审查。基于KAN的模型为临床分类提供了实用且可信赖的黑盒模型替代方案，在预测性能与可解释性之间实现了有力平衡，适用于临床决策支持。通过实现透明的患者级推理和具有临床可操作性的见解，所提模型为迈向可信医疗人工智能迈出了有前景的一步（代码：https://github.com/Patricia-A-Apellaniz/classification_with_kans）。