AI

Ontology-guided multi-agent reasoning for explainable differential diagnosis

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 28/10/2026, 04:03:25 UTC.

Briefings

A new framework combines ontology-guided reasoning with multi-agent systems to improve explainable differential diagnosis in medical AI. The system uses an expert-curated knowledge base to constrain inference and identify missing symptoms, aiming to reduce diagnostic errors caused by ambiguous patient data. The framework replicates the iterative reasoning process of physicians, generating auditable steps for clinical decisions. The source code is publicly available for further research.

Key points

• The framework uses an ontology-guided approach to constrain AI inference to evidence-backed associations, reducing hallucination and improving diagnostic transparency.
• The system identifies missing symptoms and generates targeted follow-up questions to reduce uncertainty through a process mirroring clinical inquiry.

Context

Medical AI often struggles with ambiguous patient data, leading to diagnostic errors. This research addresses the opacity of LLMs by grounding reasoning in structured expert knowledge and multi-agent interaction.

Limitations

The study is based on a preprint (arXiv:2610.36325v1) and does not include clinical trial data, patient outcomes, or validation against real-world diagnostic accuracy metrics.

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