{"slug": "llm-medical-triage-same-symptoms-gender-dependent-urgency", "title": "LLM Medical Triage: Same Symptoms, Gender-Dependent Urgency", "summary": "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.", "body_md": "# Computer Science > Artificial Intelligence\n\n[Submitted on 2 Jun 2026]\n\n# Title:Gender-Dependent Diagnostic Substitution in LLM Medical Triage: Same Symptoms, Unequal Urgency\n\n[View PDF](/pdf/2606.03641)\n\n[HTML (experimental)](https://arxiv.org/html/2606.03641v1)\n\nAbstract: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.\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth 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.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/llm-medical-triage-same-symptoms-gender-dependent-urgency", "canonical_source": "https://arxiv.org/abs/2606.03641", "published_at": "2026-06-29 08:13:00+00:00", "updated_at": "2026-06-29 08:28:53.400369+00:00", "lang": "en", "topics": ["large-language-models", "ai-safety", "ai-ethics", "artificial-intelligence"], "entities": ["Google", "Anthropic", "OpenAI", "Gemini 3.5 Flash", "Claude Sonnet 4.6", "GPT-5.4-mini", "Idiopathic Intracranial Hypertension"], "alternates": {"html": "https://wpnews.pro/news/llm-medical-triage-same-symptoms-gender-dependent-urgency", "markdown": "https://wpnews.pro/news/llm-medical-triage-same-symptoms-gender-dependent-urgency.md", "text": "https://wpnews.pro/news/llm-medical-triage-same-symptoms-gender-dependent-urgency.txt", "jsonld": "https://wpnews.pro/news/llm-medical-triage-same-symptoms-gender-dependent-urgency.jsonld"}}