arXiv:2605.01189v2 Announce Type: replace 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 between raw data and medical nomenclature. To facilitate human-aligned interaction, the system utiliz
The increasing push for AI adoption in critical sectors like healthcare, coupled with growing regulatory and ethical demands for transparency, necessitates explainable AI solutions.
Achieving 'explainability' in AI is a key bottleneck for its widespread adoption in regulated industries, directly impacting trust, safety, and liability.
The development of neuro-symbolic systems like NEURON offers a pathway to reconcile high-performance AI models with human-understandable, clinically grounded explanations, potentially accelerating AI integration in healthcare.
- · Healthcare AI Developers
- · Medical Professionals
- · Patients
- · AI Governance & Ethics Bodies
- · Pure 'black-box' AI solutions in healthcare
- · Developers ignoring explainability
- · Healthcare systems slow to adopt AI
Increased trust and adoption of AI in clinical settings as a result of improved explainability.
Faster development and deployment cycles for medical AI due to embedded interpretability, leading to new diagnostic or treatment tools.
Potential for new legal and ethical frameworks around AI liability where explainability can be algorithmically demonstrated.
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Read at arXiv cs.AI