Large Language Models (LLMs) have shown remarkable progress in medical question answering (QA), yet their effectiveness remains predominantly limited to English due to imbalanced multilingual training data and scarce medical resources for low-resource languages. To address this critical language gap in medical QA, we propose Multilingual Knowledge Graph-based Retrieval Ranking (MKG-Rank), a knowledge graph-enhanced framework that enables English-centric LLMs to perform multilingual medical QA. Through a word-level translation mechanism, our framework efficiently integrates comprehensive English-centric medical knowledge graphs into LLM reasoning at a low cost, mitigating cross-lingual semantic distortion and achieving precise medical QA across language barriers. To enhance efficiency, we introduce caching and multi-angle ranking strategies to optimize the retrieval process, significantly reducing response times and prioritizing relevant medical knowledge. Extensive evaluations on multilingual medical QA benchmarks across Chinese, Japanese, Korean, and Swahili demonstrate that MKG-Rank consistently outperforms zero-shot LLMs, achieving maximum 35.03% increase in accuracy, while maintaining an average retrieval time of only 0.0009 seconds.

翻译：大语言模型（LLMs）在医学问答（QA）领域已展现出显著进展，然而，由于多语言训练数据的不平衡以及低资源语言医学资源的稀缺，其有效性仍主要局限于英语。为应对医学问答中这一关键的语言鸿沟，我们提出了基于多语言知识图谱的检索排序框架（MKG-Rank），该框架通过知识图谱增强，使以英语为中心的大语言模型能够执行多语言医学问答。通过词级翻译机制，我们的框架能够以较低成本，将全面的英语中心医学知识图谱高效整合到大语言模型的推理过程中，从而缓解跨语言语义失真，并实现跨越语言障碍的精准医学问答。为提高效率，我们引入了缓存与多角度排序策略来优化检索过程，显著减少了响应时间并优先获取相关医学知识。在涵盖中文、日文、韩文和斯瓦希里语的多语言医学问答基准测试上进行的大量评估表明，MKG-Rank 始终优于零样本大语言模型，最高可实现 35.03% 的准确率提升，同时平均检索时间仅为 0.0009 秒。