How grounding keeps clinical AI honest
The single technique that prevents most clinical AI hallucinations.
DentistIT / Security· 5 min · Updated 30 Apr 2026
In plain language
Grounding means every claim in an AI-drafted output must point to a specific source — a transcript line, a chart entry, an image reading. A claim with no source is, by construction, a hallucination.
Why it matters
Most clinical AI failures are not subtle reasoning errors. They are claims about things the model never saw. Grounding makes that class of failure visible: if a sentence has no source, the model says nothing or marks it as unsupported.
Faurenis's approach
Faurenis attaches a source pointer to every clinical claim it drafts. The pointer is exposed in the UI — click any sentence to see where it came from. Statements without a source are marked low confidence and routed to the validation queue.
Risks & safeguards
- Spurious grounding — a model can sometimes attach the wrong source to a real claim; mitigated by structural review and held-out evaluations.
- Over-attribution — Faurenis will not invent a citation; if no source supports a claim, the model is required to omit the claim or flag it.