LLM Medical Triage: Same Symptoms, Gender-Dependent Urgency

A new study finds that large language models from Google, Anthropic, and OpenAI exhibit gender-dependent triage disparities, giving young women significantly lower emergency room referral rates than men for identical neurological symptoms. The bias stems from models substituting diagnoses based on gender-associated epidemiological priors, routing female patients to lower-urgency care despite comparable symptom severity. The findings highlight a systemic risk in AI medical triage that must be addressed by decoupling urgency assessment from probabilistic diagnostic priors.

Computer Science Artificial Intelligence Submitted on 2 Jun 2026 Title:Gender-Dependent Diagnostic Substitution in LLM Medical Triage: Same Symptoms, Unequal Urgency View PDF /pdf/2606.03641 HTML experimental https://arxiv.org/html/2606.03641v1 Abstract:We investigate whether large language models produce different medical triage recommendations for identical neurological symptoms when only the patient's stated gender and age vary. Using three model families--Gemini 3.5 Flash, Claude Sonnet 4.6, and GPT-5.4-mini--we present a standardized symptom profile persistent headache, blurred vision, morning nausea, visual disturbances across seven demographic conditions: three age groups 25, 38, 65 x two genders male, female , plus a gender-unspecified baseline n = 30 per condition per model, 630 total trials . We find a stark, systemic gender-dependent triage disparity: young women receive significantly lower emergency room ER referral rates than age-matched men Gemini: 0% vs. 23.3%; Claude: 6.7% vs. 96.7%; GPT: 6.7% vs. 66.7%, all p < 0.001 . The disparity disappears at age 65 for all models. The primary mechanism is diagnostic substitution: the models anchor on a gender-associated diagnosis, preferentially classifying young women with Idiopathic Intracranial Hypertension IIH --a condition epidemiologically linked to women of childbearing age--while diagnosing men with generic increased intracranial pressure with space-occupying lesions in the differential. This diagnostic closure routes female patients to lower-urgency care outpatient doctor appointments despite comparable severity ratings 7-9/10 . Our findings demonstrate that clinical LLMs replicate documented human clinical biases by using epidemiological priors to suppress triage urgency, suggesting that AI triage engines must decouple urgency assessment from probabilistic diagnostic priors. We release all code, prompts, and raw results. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .