Guarding Against AI Errors and Hallucinations
6 min read
Language models can invent plausible-sounding detail. The engineering and prompt safeguards that keep fabricated content out of clinical notes.
Large language models are fluent, and fluency is dangerous in a clinical setting because it makes wrong answers sound right. A model’s tendency to fill gaps with plausible invention — often called hallucination — is the defining failure mode an AI scribe has to engineer against.
Grounding in the transcript
The first defence is to constrain the model to the source material. The note should be generated from the transcript of the appointment, with explicit instructions to record only what was discussed and to leave fields at a safe baseline when the encounter did not cover them. A scribe that invents a finding is worse than one that leaves a gap for the clinician to fill.
Layered safeguards
- A model chain with fallback: if the primary model fails or returns malformed output, a second, independent model takes over — no single point of failure.
- Specialty vocabulary matching: dental terms and drug names are checked against a curated lexicon so a mis-heard word is corrected toward a real clinical term, not a confident guess.
- Inline reasoning and consistency checks: the model is asked to reason explicitly about cross-field consistency — for example, that a periodontal score, its diagnosis, and the associated risk tier all agree — surfacing contradictions rather than hiding them.
- Output validation: generated text is checked for structural integrity and stripped of artefacts before it ever reaches the clinician.
The honest framing
No safeguard makes a model infallible, and any vendor claiming otherwise should be treated with suspicion. The right goal is a system that fails safely and visibly — errors that are easy for a reviewing clinician to spot and correct — backed by the human-review step that catches what the automated checks miss.