The Effectiveness of Kolmogorov–Arnold Networks in the Healthcare Domain

Kolmogorov–Arnold Networks (KANs) have recently emerged as a powerful alternative to traditional Artificial Neural Networks (ANNs), offering superior accuracy and interpretability, which are two critical requirements in healthcare applications. This study investigates the effectiveness of KANs across a range of clinical tasks by applying them to diverse medical datasets, including structured clinical data and time-series physiological signals. Compared with conventional ANNs, KANs demonstrate significantly improved performance, achieving higher predictive accuracy even with smaller network architectures. Beyond performance gains, KANs offer a unique advantage: the ability to extract symbolic expressions from learned functions, enabling transparent, human-interpretable models—a key factor in clinical decision-making. Through comprehensive experiments and symbolic analysis, our results reveal that KANs not only outperform ANNs in modeling complex healthcare data but also provide interpretable insights that can support personalized medicine and early diagnosis. There is nothing specific about the datasets or the methods employed, so the findings are broadly applicable and position KANs as a compelling architecture for the future of AI in healthcare.