Enhancing Medical Lung X-Ray Diagnosis Through Multi-Agent Vision-Language Model Collaboration

This paper proposes a novel multi-agent framework based on Vision-Language Models (VLMs) to address challenges in medical lung X-ray diagnosis, such as diagnostic hallucinations and insufficient domain knowledge. The framework comprises three specialized agents: the WebSearch Agent for dynamic retrieval of medical knowledge, the Central Agent for multimodal reasoning and decision-making, and the Evaluation Agent for adversarial reflection and error correction. By integrating retrieval-augmented generation (RAG), toolchain functionalities, and Chain-of-Thought (CoT) optimization, the system enhances diagnostic accuracy, interpretability, and robustness. Empirical validation demonstrates the superiority of this collaborative approach over single-agent systems, offering a scalable solution for complex medical diagnostics. The contributions include a modular architecture, dynamic knowledge fusion, and reflection mechanisms, significantly reducing diagnostic errors and improving clinical reliability.