LLMs for Medical Consultation Are Evaluated Too Late: The Preformulation Gap
Yining Hua, Cyrus Ayubcha, Hongbin Na, Levi Lian, Alon Gorenshtein, Yiftach Barash, Eyal Klang
Abstract
Large language models for medical consultation are often evaluated after a clinical problem has already been made clear, although real consultations may begin with a vague, minimized, or misframed concern. We evaluated three API models across four physician-authored, multi-turn vignettes under baseline and entry-to-care instruction conditions, yielding 24 fixed-script transcripts; two cases also used adaptive standardized-patient simulation, yielding 12 transcripts. Self-care or home-management advice before any patient answer appeared in 9 of 12 baseline case-model cells and 0 of 12 instruction cells, while structured handoff summaries appeared in 0 of 12 and 10 of 12 cells, respectively. The instruction changed sequencing and documentation, although it did not reliably ensure elicitation of decisive facts. The preformulation gap should therefore be evaluated directly through observable first-contact behavior rather than inferred from diagnostic accuracy or final-answer quality.
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Classified with taxonomy v2 on Wed, 2 Sept 2026.