EQUITRIAGE: A Fairness Audit of Gender Bias in LLM-Based Emergency Department Triage
Emergency department triage assigns patients an acuity score that determines treatment priority, and clinical evidence documents persistent gender disparities in human acuity assessment. As hospitals pilot large language models (LLMs) as triage decision support, a critical question is whether these models reproduce or mitigate known biases. We present EQUITRIAGE, a fairness audit of LLM-based ESI assignment evaluating five models (Gemini-3-Flash, Nemotron-3-Super, DeepSeek-V3.1, Mistral-Small-3.
Record details
Published: 5 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare · Transparency
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (5 May 2026), “EQUITRIAGE: A Fairness Audit of Gender Bias in LLM-Based Emergency Department Triage,” evidence record 4952, https://ethics.ai/record/4952 (originally published by arXiv).
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