Using mathematically interpretable neural network architectures (KANs) as the foundation for medical AI explanations produces more trustworthy and faithful results than adding language models on top of opaque vision models.
KANEx uses Kolmogorov-Arnold Networks (KANs)—models with interpretable spline-based components—to improve medical AI explainability. Instead of relying on black-box vision models paired with language models, the framework grounds textual explanations in KAN's transparent mathematical structure and introduces KAN-Map for more faithful visual explanations.