RadAgent:用于逐步解读胸部CT的工具使用型AI智能体
阅读原文· arxiv.org研究团队推出RadAgent,一款用于胸部CT解读的工具使用型AI智能体。该系统通过逐步推理生成报告,提供可审查的决策轨迹与工具交互记录。相比3D VLM基线CT-Chat,其临床准确性macro-F1提升6.0分(36.4%)、micro-F1提升5.4分(19.6%),对抗鲁棒性提升24.7分(41.9%),并首次实现37.0%的忠实度指标,显著提升了放射学AI的透明度与可靠性。
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.