Large language models (LLMs) have demonstrated strong capabilities in medical question answering; however, purely parametric models often suffer from knowledge gaps and limited factual grounding. Retrieval-augmented generation (RAG) addresses this limitation by integrating external knowledge retrieval into the reasoning process. Despite increasing interest in RAG-based medical systems, the impact of individual retrieval components on performance remains insufficiently understood. This study presents a systematic evaluation of retrieval-augmented medical question answering using the MedQA USMLE benchmark and a structured textbook-based knowledge corpus. We analyze the interaction between language models, embedding models, retrieval strategies, query reformulation, and cross-encoder reranking within a unified experimental framework comprising forty configurations. Results show that retrieval augmentation significantly improves zero-shot medical question answering performance. The best-performing configuration was dense retrieval with query reformulation and reranking achieved 60.49% accuracy. Domain-specialized language models were also found to better utilize retrieved medical evidence than general-purpose models. The analysis further reveals a clear tradeoff between retrieval effectiveness and computational cost, with simpler dense retrieval configurations providing strong performance while maintaining higher throughput. All experiments were conducted on a single consumer-grade GPU, demonstrating that systematic evaluation of retrieval-augmented medical QA systems can be performed under modest computational resources.

翻译：大语言模型在医学问答中展现出强大能力，但纯参数模型常受限于知识缺口与事实依据不足。检索增强生成通过将外部知识检索融入推理过程解决了这一局限。尽管基于检索增强的医学系统日益受到关注，但各检索组件对性能的具体影响仍未被充分理解。本研究基于MedQA USMLE基准测试与结构化教科书知识语料库，对检索增强型医学问答展开系统评估。我们在包含四十种配置的统一实验框架下，分析了语言模型、嵌入模型、检索策略、查询重构以及交叉编码器重排序之间的交互作用。结果显示，检索增强显著提升了零样本医学问答性能，其中采用查询重构与重排序的密集检索配置达到60.49%的最高准确率。领域专用语言模型比通用模型更能有效利用检索到的医学证据。分析进一步揭示了检索效率与计算成本之间的明确权衡——简单密集检索配置在保持高吞吐量的同时仍能提供强劲性能。所有实验均基于单块消费级GPU完成，表明在适度计算资源下即可实现对检索增强型医学问答系统的系统性评估。