让AI自己判断何时该出诊断、何时该求助，更靠谱地筛肺部X光片。
AT-CXR: Uncertainty-Aware Agentic Triage for Chest X-rays
- 根据每张片子的不确定性和分布拟合度，自动决定是否出结果或转人工。
- 在五折交叉验证中，准确率和选判性能均优于主流模型，高覆盖率下错误率更低。
- 支持高吞吐或高精度两种模式，适合临床实际场景部署。
代理型人工智能发展迅速，但真正能自主完成医学影像分诊——在真实约束下决定何时停止、升级或延迟处理——仍相对缺乏研究。为此，我们提出AT-CXR，一种针对胸部X光片的不确定性感知代理系统。该系统评估每例病例的置信度与分布拟合程度，并采用分步策略作出自动化决策或主动放弃，同时建议人工介入标签。我们在NIH ChestX-ray14数据集的平衡子集上进行五折评估，比较了两种共享输入与动作的路由设计：基于规则的确定性路由器和由大语言模型决定的路由器。两种变体均优于强大的零样本视觉-语言模型及当前最先进的监督分类器，在全覆盖准确率和选择性预测性能上表现更优，体现在更低的风险-覆盖率曲线下面积（AURC）和高覆盖率下的更低误差率，且延迟更低，满足临床实际要求。两种路由器提供互补的工作点，可按需侧重吞吐量或准确率。代码已开源：https://github.com/XLIAaron/uncertainty-aware-cxr-agent。
原文摘要 · Abstract (English)
Agentic AI is advancing rapidly, yet truly autonomous medical-imaging triage, where a system decides when to stop, escalate, or defer under real constraints, remains relatively underexplored. To address this gap, we introduce AT-CXR, an uncertainty-aware agent for chest X-rays. The system estimates per-case confidence and distributional fit, then follows a stepwise policy to issue an automated decision or abstain with a suggested label for human intervention. We evaluate two router designs that share the same inputs and actions: a deterministic rule-based router and an LLM-decided router. Across five-fold evaluation on a balanced subset of NIH ChestX-ray14 dataset, both variants outperform strong zero-shot vision-language models and state-of-the-art supervised classifiers, achieving higher full-coverage accuracy and superior selective-prediction performance, evidenced by a lower area under the risk-coverage curve (AURC) and a lower error rate at high coverage, while operating with lower latency that meets practical clinical constraints. The two routers provide complementary operating points, enabling deployments to prioritize maximal throughput or maximal accuracy. Our code is available at https://github.com/XLIAaron/uncertainty-aware-cxr-agent.
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