Unmasking and quantifying racial bias of large language models in medical report generation.

ABSTRACT:

Background

Large language models like GPT-3.5-turbo and GPT-4 hold promise for healthcare professionals, but they may inadvertently inherit biases during their training, potentially affecting their utility in medical applications. Despite few attempts in the past, the precise impact and extent of these biases remain uncertain.

Methods

We use LLMs to generate responses that predict hospitalization, cost and mortality based on real patient cases. We manually examine the generated responses to identify biases.

Results

We find that these models tend to project higher costs and longer hospitalizations for white populations and exhibit optimistic views in challenging medical scenarios with much higher survival rates. These biases, which mirror real-world healthcare disparit

SUBMITTER: Yang Y 

PROVIDER: S-EPMC11387737 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature