Identifying Bias at Scale in Clinical Notes Using Large Language Models

The study showed that GPT-4 accurately detects biased language in clinical notes, identifies modifiable contributors to that bias, and delivers physician-endorsed revisions. This approach may help mitigate documentation bias and reduce disparities in care.

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Publication Details

Journal
Mayo Clinic Proceedings Digital Health
Published
2025-10-15
DOI
https://doi.org/10.1016/j.mcpdig.2025.100296
Citations
1
Primary Topic
Health Promotion and Cardiovascular Prevention
Type
article
Field-Weighted Citation Impact
1.88
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article

Identifying Bias at Scale in Clinical Notes Using Large Language Models

Arash Negahdari Kia, Donald U. Apakama, Kim-Anh-Nhi Nguyen, Rebecca Anderson et al.
1 citations
Mayo Clinic Proceedings Digital Health
Health Promotion and Cardiovascular Prevention
1.88
article

Identifying Bias at Scale in Clinical Notes Using Large Language Models

Arash Negahdari Kia, Donald U. Apakama, Kim-Anh-Nhi Nguyen, Rebecca Anderson, Rohan Khera, Patricia Kovatch, Allison Glasser, Prem Timsina, Brendan G. Carr, Clair Kronk, Prathamesh Parchure, Eyal Klang, Girish N. Nadkarni, Masoud Edalati, Lili Chan, Robert Freeman, Lynne D. Richardson, Shelly Soffer, Matthew A. Levin, Alexander W. Charney, Aya Mudrik, Allon E. Moses, Carol R. Horowitz, E. Ling, Evajoyce Woullard, Daphnee Hyppolite, Ivanka Temnycky
article en
1 citations

Abstract

The study showed that GPT-4 accurately detects biased language in clinical notes, identifies modifiable contributors to that bias, and delivers physician-endorsed revisions. This approach may help mitigate documentation bias and reduce disparities in care.

Mayo Clinic Proceedings Digital HealthVol. 3(4)
Ben-Gurion University of the Negev (IL), Mount Sinai Health System (US), Rabin Medical Center (IL), Yale University (US), Icahn School of Medicine at Mount Sinai (US)
Openalex Percentile: Top 11%
Health Promotion and Cardiovascular Prevention
1.88
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