NEURON combines SNOMED CT ontology grounding with machine learning to make clinical predictions more explainable. It is relevant for builders working on trustworthy medical AI, though the contribution appears narrower than general-purpose interpretability research.
arXiv:2605.01189v1 Announce Type: new Abstract: Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability. We present NEURON, a neuro-symbolic system designed to enhance both predictive reliability and clinical interpretability. NEURON integrates SNOMED CT ontology-informed structural representations with machine learning models to bridge the gap…