Auditing Sex/Gender Disparities in Emergency Triage with LLM-based Paired Comparisons

A research paper from arXiv introduces a novel, domain-agnostic method utilizing Large Language Models (LLMs) to identify and quantify sex/gender disparities within documented clinical decision-making. This approach involves training an LLM to mimic observed decisions and then analyzing sex-swapped patient profiles where only the sex is altered. Initial application to emergency triage data from Bordeaux University Hospital and validation on MIMIC-IV datasets revealed that otherwise identical presentations were more likely to receive a lower triage priority when sex/gender was modified.

This research is strategically important because it provides a new, scalable method for identifying systemic biases in critical decision-making processes, particularly in domains like healthcare. Understanding and mitigating such biases is crucial for maintaining equity, improving operational integrity, and ensuring compliance with ethical and regulatory standards.

A new domain-agnostic method employs LLMs to quantify sex/gender-related asymmetries in clinical decision-making.

Forward consideration, not a verified fact.