RadAgent：一种用于胸部计算机断层扫描逐步解析的工具型AI代理

摘要

视觉语言模型（VLM）显著推动了人工智能在复杂医学影像（如计算机断层扫描CT）解读与报告生成方面的发展。然而，现有方法大多将临床医生置于最终输出的被动观察者角色，未能提供可解释的推理路径供其审查、验证或修正。为此，我们推出RadAgent——一个运用工具的人工智能代理，通过可解释的渐进式流程生成CT报告。每份生成报告均附带完整可追溯的中间决策与工具交互路径，使临床医生能够核查报告结论的推导过程。实验表明，RadAgent在胸部CT报告生成任务中较其三维视觉语言模型对照系统CT-Chat实现三维度提升：临床准确性方面，宏观F1分数提升6.0分（相对提升36.4%），微观F1分数提升5.4分（相对提升19.6%）；对抗条件下的鲁棒性提升24.7分（相对提升41.9%）；此外，RadAgent在事实一致性维度达到37.0%的指标，而该能力在其三维视觉语言模型对照系统中完全缺失。通过将胸部CT影像解读构建为显式、工具增强的迭代推理轨迹，RadAgent使放射学领域向透明可靠的人工智能迈出关键一步。

English

Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT). Yet, existing methods largely relegate clinicians to passive observers of final outputs, offering no interpretable reasoning trace for them to inspect, validate, or refine. To address this, we introduce RadAgent, a tool-using AI agent that generates CT reports through a stepwise and interpretable process. Each resulting report is accompanied by a fully inspectable trace of intermediate decisions and tool interactions, allowing clinicians to examine how the reported findings are derived. In our experiments, we observe that RadAgent improves Chest CT report generation over its 3D VLM counterpart, CT-Chat, across three dimensions. Clinical accuracy improves by 6.0 points (36.4% relative) in macro-F1 and 5.4 points (19.6% relative) in micro-F1. Robustness under adversarial conditions improves by 24.7 points (41.9% relative). Furthermore, RadAgent achieves 37.0% in faithfulness, a new capability entirely absent in its 3D VLM counterpart. By structuring the interpretation of chest CT as an explicit, tool-augmented and iterative reasoning trace, RadAgent brings us closer toward transparent and reliable AI for radiology.

RadAgent：一种用于胸部计算机断层扫描逐步解析的工具型AI代理

RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography

摘要

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