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

• URL: http://arxiv.org/abs/2508.02841v1
• Date: Mon, 04 Aug 2025 19:09:52 GMT
• Title: A Multi-Agent System for Complex Reasoning in Radiology Visual Question Answering
• Authors: Ziruo Yi, Jinyu Liu, Ting Xiao, Mark V. Albert,
• Abstract summary: Radiology visual question answering (RVQA) provides precise answers to questions about chest X-ray images.<n>Recent methods based on multimodal large language models (MLLMs) and retrieval-augmented generation (RAG) have shown promising progress in RVQA.<n>We introduce a multi-agent system (MAS) designed to support complex reasoning in RVQA.
• Score: 3.3809462259925938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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