用多智能体RAG+概念瓶颈模型，让放射科报告生成更可解释、更可信。
CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models
- 结合概念瓶颈模型与多智能体RAG，将影像特征映射为可理解的临床概念。
- 生成的报告具证据支持，显著减少幻觉，提升诊断可靠性。
- 适合关注AI可解释性与临床落地的医学AI研究者和放射科医生。
生成式人工智能在自动化放射科工作流方面前景广阔，但可解释性与可靠性问题限制了其临床应用。本文提出一种自动放射科报告生成框架，结合概念瓶颈模型（CBMs）与多智能体检索增强生成（RAG）系统，实现性能与临床可解释性的统一。CBMs将胸部X光片特征映射为人类可理解的临床概念，实现透明的疾病分类。多智能体RAG系统融合多智能体协作与外部知识，生成上下文丰富、基于证据的报告。演示结果表明，该系统能提供可解释的预测，减少幻觉，生成高质量、定制化报告，并通过交互式界面解决准确性、信任度与可用性挑战。该框架为提升诊断一致性、赋能放射科医生提供可操作洞察提供了可行路径。
原文摘要 · Abstract (English)
Advancements in generative Artificial Intelligence (AI) hold great promise for automating radiology workflows, yet challenges in interpretability and reliability hinder clinical adoption. This paper presents an automated radiology report generation framework that combines Concept Bottleneck Models (CBMs) with a Multi-Agent Retrieval-Augmented Generation (RAG) system to bridge AI performance with clinical explainability. CBMs map chest X-ray features to human-understandable clinical concepts, enabling transparent disease classification. Meanwhile, the RAG system integrates multi-agent collaboration and external knowledge to produce contextually rich, evidence-based reports. Our demonstration showcases the system's ability to deliver interpretable predictions, mitigate hallucinations, and generate high-quality, tailored reports with an interactive interface addressing accuracy, trust, and usability challenges. This framework provides a pathway to improving diagnostic consistency and empowering radiologists with actionable insights.
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