FAQ
How is bias measured in clinical LLMs?
Bias is evaluated using clinical vignettes and “counterfactual” variations (e.g., changing patient attributes) to observe differential responses, allowing detection of both performance disparities and fairness issues across demographic groups.
How common are demographic biases in healthcare LLM outputs?
Systematic reviews reveal pervasive demographic bias, especially across race, ethnicity, gender, age, and disability, affecting tasks like trial matching and question answering—suggesting biased care recommendations.
What types of bias do LLMs exhibit in clinical decision-making?
Bias can manifest as allocative harm (e.g., fewer diagnostic tests for certain groups), representational bias (using stereotypes), and performance disparities—like lower accuracy or recommendation quality for some demographics.
What methods exist to mitigate clinical LLM bias?
Techniques include prompt engineering, fine-tuning, contrastive learning frameworks like EquityGuard, and multi-agent chain-of-thought reasoning—all shown to reduce bias in medical question answering and trial matching tasks.
Who should conduct bias testing of LLMs before clinical use?
Bias testing should be done by developers and healthcare institutions using structured protocols and benchmarks like CLIMB, DiversityMedQA, or CPV datasets to ensure robust validation across diverse patient populations.
