ArticleOtolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery2026
The Utility of Large Language Models to Assist With Emergency Triage Decisions Within Otolaryngology.
Article in Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo determine whether contemporary large language models can match clinician performance in evaluating the urgency of emergency otolaryngology referrals. STUDY
designBlinded cross-sectional diagnostic reasoning study.
settingSimulated emergency referral environment modeled on tertiary care otolaryngology practice.
methodsThirty emergency referral scenarios spanning the spectrum of otolaryngologic urgency were independently evaluated by 4 large language models (GPT-5, GPT-4, DeepSeek, and Grok) and 4 clinicians (otolaryngology attending and resident, emergency attending and resident). Outputs were anonymized and scored by 10 blinded otolaryngologists for appropriateness of urgency and quality of explanation using a three-point scale. Statistical analyses included nonparametric group comparisons, adjusted ordinary least squares modeling with case-level control, and correlation of each entity's case profile with that of the otolaryngology attending.
resultsInter-rater reliability was excellent. The otolaryngology attending achieved the highest overall performance. GPT-5 demonstrated comparable mean performance, with no statistically significant difference in either domain. GPT-4 scored modestly lower but received higher mean ratings than both emergency clinicians. DeepSeek and the otolaryngology resident demonstrated intermediate performance, while Grok and the emergency clinicians performed lowest. Group-level analyses showed no significant difference between the large language model and otolaryngology cohorts; both were rated higher than emergency clinicians in this sample.
conclusionGPT-5 demonstrated triage performance comparable to the otolaryngology attending in this controlled sample. Large language models may support emergency decision-making and education when specialist consultation is limited, but require supervision, transparency, and local calibration.
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