RadFabric：具备推理能力的放射学智能体AI系统
RadFabric: Agentic AI System with Reasoning Capability for Radiology
AI总结：
针对胸部X射线自动化诊断系统的局限，研究提出基于MCP的多智能体多模态推理框架RadFabric，整合多类智能体与推理模型，实现高精度、透明可解释的CXR诊断，性能显著优于传统系统。
摘要：
胸部X射线（CXR）成像仍是胸部疾病的关键诊断工具，但当前自动化系统在病变覆盖范围、诊断准确性以及视觉与文本推理的整合方面存在局限。为弥补这些不足，我们提出RadFabric，这是一种多智能体多模态推理框架，可统一视觉与文本分析以实现全面的CXR解读。RadFabric基于模型上下文协议（Model Context Protocol，MCP）构建，具备模块化、互操作性与可扩展性，可无缝集成新的诊断智能体。该系统采用专门的CXR智能体进行病变检测，配备解剖学解读智能体将视觉发现映射到精确的解剖结构，并搭载由大型多模态推理模型驱动的推理智能体，将视觉、解剖学与临床数据综合为透明且基于证据的诊断结果。RadFabric实现了显著的性能提升：对骨折等疑难病变的检测近乎完美（准确率1.000），整体诊断准确率（0.799）优于传统系统（0.229至0.527）。通过整合跨模态特征对齐与偏好驱动推理，RadFabric推动AI驱动的放射学向透明、解剖学精准且具备临床可操作性的CXR分析方向发展。
英文摘要：
Chest X ray (CXR) imaging remains a critical diagnostic tool for thoracic conditions, but current automated systems face limitations in pathology coverage, diagnostic accuracy, and integration of visual and textual reasoning. To address these gaps, we propose RadFabric, a multi agent, multimodal reasoning framework that unifies visual and textual analysis for comprehensive CXR interpretation. RadFabric is built on the Model Context Protocol (MCP), enabling modularity, interoperability, and scalability for seamless integration of new diagnostic agents. The system employs specialized CXR agents for pathology detection, an Anatomical Interpretation Agent to map visual findings to precise anatomical structures, and a Reasoning Agent powered by large multimodal reasoning models to synthesize visual, anatomical, and clinical data into transparent and evidence based diagnoses. RadFabric achieves significant performance improvements, with near-perfect detection of challenging pathologies like fractures (1.000 accuracy) and superior overall diagnostic accuracy (0.799) compared to traditional systems (0.229 to 0.527). By integrating cross modal feature alignment and preference-driven reasoning, RadFabric advances AI-driven radiology toward transparent, anatomically precise, and clinically actionable CXR analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
