RadFabric用多智能体系统实现胸部X光的精准诊断与推理。
RadFabric: Agentic AI System with Reasoning Capability for Radiology
- 构建多智能体框架，融合视觉与文本推理分析
- 对骨折等病灶检测准确率达1.000，整体诊断准确率0.799
- 适合临床辅助诊断与可解释性研究场景
胸部X光仍是胸腔疾病诊断的关键工具，但现有自动化系统在病灶覆盖、诊断准确率及视觉与文本推理整合方面存在局限。为此，我们提出RadFabric，一个基于模型上下文协议（MCP）的多智能体多模态推理框架，统一进行视觉与文本分析，实现全面的胸部X光解读。该系统包含专门用于病灶检测的CXG智能体、将影像发现映射到精确解剖结构的解剖解读智能体，以及由大模型驱动的推理智能体，综合视觉、解剖和临床数据生成透明且有依据的诊断。实验表明，RadFabric在复杂病灶如骨折的检测上达到1.000的准确率，整体诊断准确率高达0.799，显著优于传统系统（0.229至0.527）。通过跨模态特征对齐与偏好驱动推理，该系统推动了面向透明、解剖精准、临床可操作的胸部X光智能分析发展。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。