Enhancing Equity in Large Language Models for Medical Applications
<|reference_start|>Enhancing Equity in Large Language Models for Medical Applications: Recent advancements have highlighted the potential of large language models (LLMs) in medical applications, notably in automating Clinical Trial Matching for translational research and providing medical question-answering for clinical decision support. However, our study reveals significant inequities in the use of LLMs, particularly for individuals from specific racial, gender, and underrepresented groups influenced by social determinants of health. These disparities could worsen existing health inequities if LLMs are broadly adopted in healthcare. To address this, we propose and evaluate a novel framework, EquityGuard, designed to detect and mitigate biases in LLM-based medical applications. EquityGuard incorporates a Bias Detection Mechanism capable of identifying and correcting unfair predictions, thus enhancing outcomes and promoting equity across diverse population groups.<|reference_end|>
@article{ji2024mitigating,
    title={Mitigating the Risk of Health Inequity Exacerbated by Large Language
  Models},
    author={Yuelyu Ji, Wenhe Ma, Sonish Sivarajkumar, Hang Zhang, Eugene Mathew
  Sadhu, Zhuochun Li, Xizhi Wu, Shyam Visweswaran, and Yanshan Wang},
    journal={arXiv preprint arXiv:2410.05180},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05180},
    primaryClass={cs.CL}
}