{
  "id": 5089,
  "url": "https://arxiv.org/abs/2605.01189v2",
  "title": "NEURON: A Neuro-symbolic System for Grounded Clinical Explainability",
  "summary": "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 between raw data and medical nomenclature. To facilit",
  "authors": "Anuradha Chandrasekaran, Dimitrios Zikos, Mutlu Mete, Alan Pang, Brady D. Lund, Kewei Sha",
  "category": "research",
  "topics": "safety-alignment,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-02T02:00:02.000Z",
  "fetched_at": "2026-07-14T16:31:31.210Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5089",
  "original_url": "https://arxiv.org/abs/2605.01189v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}