标题： 放射学视觉问答中的复杂推理多智能体系统 显示英文标题

标题： A Multi-Agent System for Complex Reasoning in Radiology Visual Question Answering

摘要： 放射科视觉问答（RVQA）能够对胸部X光图像提出的问题提供精确答案，减轻放射科医生的工作负担。 尽管基于多模态大语言模型（MLLMs）和检索增强生成（RAG）的最新方法在RVQA中表现出有前景的进展，但它们在事实准确性、幻觉和跨模态不对齐方面仍面临挑战。 我们引入了一个多智能体系统（MAS），旨在支持RVQA中的复杂推理，包括专门用于上下文理解、多模态推理和答案验证的智能体。 我们在一个通过模型分歧过滤整理的具有挑战性的RVQA数据集上评估了我们的系统，该数据集包含多个MLLMs中持续困难的案例。 大量实验表明，我们的系统在强MLLM基线之上表现出优越性和有效性，案例研究展示了其可靠性和可解释性。 这项工作突显了多智能体方法在支持需要复杂推理的可解释和值得信赖的临床AI应用方面的潜力。 显示英文摘要

摘要： Radiology visual question answering (RVQA) provides precise answers to questions about chest X-ray images, alleviating radiologists' workload. While recent methods based on multimodal large language models (MLLMs) and retrieval-augmented generation (RAG) have shown promising progress in RVQA, they still face challenges in factual accuracy, hallucinations, and cross-modal misalignment. We introduce a multi-agent system (MAS) designed to support complex reasoning in RVQA, with specialized agents for context understanding, multimodal reasoning, and answer validation. We evaluate our system on a challenging RVQA set curated via model disagreement filtering, comprising consistently hard cases across multiple MLLMs. Extensive experiments demonstrate the superiority and effectiveness of our system over strong MLLM baselines, with a case study illustrating its reliability and interpretability. This work highlights the potential of multi-agent approaches to support explainable and trustworthy clinical AI applications that require complex reasoning.