让AI像医生一样看胸片，边看边推理，还能自我检查纠错。
RadAgents: Multimodal Agentic Reasoning for Chest X-ray Interpretation with Radiologist-like Workflows
- 用多个专业AI agent协作，模仿医生读片流程
- 融合图像与文字信息，输出有视觉依据的诊断理由
- 能发现不同工具间的矛盾并自动验证，适合临床可信场景
智能体系统通过多智能体协作和工具使用，有望解决复杂临床任务。然而，现有胸片（CXR）解读方法仍存在三大不足：(i) 推理过程缺乏临床可解释性且不符合指南，仅是工具输出的简单拼接；(ii) 多模态证据融合不足，导致解释仅为文本描述，缺乏视觉支撑；(iii) 很少检测或解决跨工具不一致问题，缺乏系统性验证机制。为此，我们提出RadAgents，一种结合临床先验知识与任务感知多模态推理的多智能体框架，将放射科医生的工作流程编码为模块化、可审计的处理管线。同时，集成视觉定位与多模态检索增强技术，实现上下文冲突的验证与修复，最终输出更可靠、透明且符合临床实践的诊断结果。
原文摘要 · Abstract (English)
Agentic systems offer a potential path to solve complex clinical tasks through collaboration among specialized agents, augmented by tool use and external knowledge bases. Nevertheless, for chest X-ray (CXR) interpretation, prevailing methods remain limited: (i) reasoning is frequently neither clinically interpretable nor aligned with guidelines, reflecting mere aggregation of tool outputs; (ii) multimodal evidence is insufficiently fused, yielding text-only rationales that are not visually grounded; and (iii) systems rarely detect or resolve cross-tool inconsistencies and provide no principled verification mechanisms. To bridge the above gaps, we present RadAgents, a multi-agent framework that couples clinical priors with task-aware multimodal reasoning and encodes a radiologist-style workflow into a modular, auditable pipeline. In addition, we integrate grounding and multimodal retrieval-augmentation to verify and resolve context conflicts, resulting in outputs that are more reliable, transparent, and consistent with clinical practice.
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