Evaluating and addressing demographic disparities in medical large language models: a systematic review.

ABSTRACT:

Background

Large language models are increasingly evaluated for use in healthcare. However, concerns about their impact on disparities persist. This study reviews current research on demographic biases in large language models to identify prevalent bias types, assess measurement methods, and evaluate mitigation strategies.

Methods

We conducted a systematic review, searching publications from January 2018 to July 2024 across five databases. We included peer-reviewed studies evaluating demographic biases in large language models, focusing on gender, race, ethnicity, age, and other factors. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools.

Results

Our review included 24 studies. Of these, 22 (91.7%) identified biases. Gender bias was

SUBMITTER: Omar M 

PROVIDER: S-EPMC11866893 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature