Medical Question Answering systems based on Retrieval Augmented Generation is promising for clinical decision support because they can integrate external knowledge, thus reducing inaccuracies inherent in standalone large language models (LLMs). However, these systems may unintentionally propagate or amplify biases associated with sensitive demographic attributes like race, gender, and socioeconomic factors. This study systematically evaluates demographic biases within medical RAG pipelines across multiple QA benchmarks, including MedQA, MedMCQA, MMLU, and EquityMedQA. We quantify disparities in retrieval consistency and answer correctness by generating and analyzing queries sensitive to demographic variations. We further implement and compare several bias mitigation strategies to address identified biases, including Chain of Thought reasoning, Counterfactual filtering, Adversarial prompt refinement, and Majority Vote aggregation. Experimental results reveal significant demographic disparities, highlighting that Majority Vote aggregation notably improves accuracy and fairness metrics. Our findings underscore the critical need for explicitly fairness-aware retrieval methods and prompt engineering strategies to develop truly equitable medical QA systems.

翻译：基于检索增强生成（RAG）的医疗问答系统在临床决策支持方面前景广阔，因为它们能够整合外部知识，从而减少独立大型语言模型（LLMs）固有的不准确性。然而，这些系统可能会无意中传播或放大与种族、性别和社会经济因素等敏感人口属性相关的偏见。本研究系统评估了多个问答基准（包括MedQA、MedMCQA、MMLU和EquityMedQA）中医疗RAG流程内的人口统计学偏见。我们通过生成和分析对人口统计学变化敏感的查询，量化了检索一致性和答案正确性方面的差异。我们进一步实施并比较了多种偏见缓解策略以应对已识别的偏见，包括思维链推理、反事实过滤、对抗性提示优化和多数投票聚合。实验结果显示显著的人口统计学差异，并突显多数投票聚合能显著提升准确性和公平性指标。我们的研究结果强调，迫切需要明确具有公平性意识的检索方法和提示工程策略，以开发真正公平的医疗问答系统。