Identifying Bias at Scale in Clinical Notes Using Large Language Models.

ABSTRACT:

Objective

To evaluate whether generative pretrained transformer (GPT)-4 can detect and revise biased language in emergency department (ED) notes, against human-adjudicated gold-standard labels, and to identify modifiable factors associated with biased documentation.

Patients and methods

We randomly sampled 50,000 ED medical and nursing notes from the Mount Sinai Health System (January 1, 2023, to December 31, 2023). We also randomly sampled 500 discharge notes from the Medical Information Mart for Intensive Care IV database. The GPT-4 flagged 4 types of bias: discrediting, stigmatizing/labeling, judgmental, and stereotyping. Two human reviewers verified model detections. We used multivariable logistic regression to examine associations between bias and health care utilizatio

SUBMITTER: Apakama DU 

PROVIDER: S-EPMC12666851 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature