AI导师通过多智能体协作，实时指导放射科住院医师解读胸部X光片。
IMACT-CXR: An Interactive Multi-Agent Conversational Tutoring System for Chest X-Ray Interpretation
- 多智能体协同分析定位框、注视点与文字描述，实现交互式教学
- 相比基线模型，定位准确率和诊断推理能力显著提升
- 适合医学教育场景，支持真实DICOM数据与临床部署
IMACT-CXR 是一个基于 AutoGen 的多智能体对话式教学系统，用于辅助训练者解读胸部 X 光片。系统整合空间标注、注视分析、知识检索与图像引导推理，在单一工作流中实现多模态输入处理。该系统同时接收学习者的边界框、注视采样及自由文本观察，由专用智能体评估定位质量、生成苏格拉底式辅导、从 PubMed 检索证据、从 REFLACX 数据集推荐相似病例，并在掌握度不足或用户主动请求时调用 NV-Reason-CXR-3B 进行视觉-语言推理。贝叶斯知识追踪（BKT）动态维护各技能项的掌握度估计，驱动知识强化与病例相似性检索。基于 TensorFlow U-Net 的肺段分割模块提供解剖学感知的注视反馈，安全提示机制防止过早泄露真实标签。本文描述了系统架构、实现亮点及其与 REFLACX 数据集的集成，适用于真实 DICOM 病例。初步评估表明，IMACT-CXR 具有响应迅速、延迟可控、答案泄露可精准控制，并具备向实际住院医师培训系统扩展的潜力。相较于基线方法，其在定位精度与诊断推理能力上均有提升。
原文摘要 · Abstract (English)
IMACT-CXR is an interactive multi-agent conversational tutor that helps trainees interpret chest X-rays by unifying spatial annotation, gaze analysis, knowledge retrieval, and image-grounded reasoning in a single AutoGen-based workflow. The tutor simultaneously ingests learner bounding boxes, gaze samples, and free-text observations. Specialized agents evaluate localization quality, generate Socratic coaching, retrieve PubMed evidence, suggest similar cases from REFLACX, and trigger NV-Reason-CXR-3B for vision-language reasoning when mastery remains low or the learner explicitly asks. Bayesian Knowledge Tracing (BKT) maintains skill-specific mastery estimates that drive both knowledge reinforcement and case similarity retrieval. A lung-lobe segmentation module derived from a TensorFlow U-Net enables anatomically aware gaze feedback, and safety prompts prevent premature disclosure of ground-truth labels. We describe the system architecture, implementation highlights, and integration with the REFLACX dataset for real DICOM cases. IMACT-CXR demonstrates responsive tutoring flows with bounded latency, precise control over answer leakage, and extensibility toward live residency deployment. Preliminary evaluation shows improved localization and diagnostic reasoning compared to baselines.
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