Evaluation format, not model capability, drives measured triage failure in the assessment of consumer health AI
A recent Nature Medicine study reported that ChatGPT Health under-triages 51.6% of emergencies and concluded that consumer-facing AI triage poses safety risks. Its protocol, however, was an exam-style scaffold (forced A/B/C/D output, knowledge suppression, no clarifying questions) unlike how consumers use health chatbots. We ask whether the headline error rate is a property of the models or of the measurement. In a first, mechanistic study, five frontier LLMs on a 17-scenario bank scored 6.4 points higher under naturalistic patient-style messages than under the constrained scaffold (p=0.015), and on one vignette three models went from 0-24% with forced choice to 100% with free text. In a ...
| Authors | David Fraile Navarro, Jialei Sheng, Farah Magrabi et al. |
|---|---|
| Primary category | cs.HC |
| Published | 12 March 2026 |
| Last updated at source | 2 October 2026 |
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