Gender-Dependent Diagnostic Substitution in LLM Medical Triage: Same Symptoms, Unequal Urgency
arXiv:2606.03641v1 Announce Type: new 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 t
The proliferation and increasing reliance on large language models in sensitive applications like healthcare make evaluating their biases, particularly gender bias, a critical and timely concern.
This research highlights potential inherent biases in leading AI models, which could lead to disparate and unequal outcomes in critical services like medical triage, impacting public trust and fairness.
The understanding of AI's potential to perpetuate or amplify societal biases is deepened, underscoring the need for more rigorous bias detection and mitigation strategies in AI development.
- · AI ethics researchers
- · Healthcare regulatory bodies
- · Patients advocating for equitable care
- · Providers of biased AI medical triage systems
- · Patients receiving unequal treatment
- · Unregulated AI deployment in sensitive sectors
Public scrutiny and pressure increase on AI developers to address bias in their models.
Regulatory frameworks for AI in healthcare are strengthened to mandate fairness and bias audits.
Investment shifts towards explainable AI and inherently debiased model architectures, potentially delaying broader AI adoption in critical sectors until robust solutions are found.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI