Unmasking and Quantifying Racial Bias of Large Language Models in
  Medical Report Generation
Yifan Yang Xiaoyu Liu Qiao Jin Furong Huang Zhiyong Lu
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. Through both qualitative and quantitative analyses, 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 disparities, are evident in the generation of patient backgrounds,
the association of specific diseases with certain races, and disparities in
treatment recommendations, etc. Our findings underscore the critical need for
future research to address and mitigate biases in language models, especially
in critical healthcare applications, to ensure fair and accurate outcomes for
all patients.