发表机构
University of Notre Dame（圣母大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出CRC-Router，一种风险约束、不确定性感知的路由模块，通过共形风险控制校准接受阈值，在医学影像分诊中实现最优风险-覆盖率权衡，并可与智能体系统集成。
AI 中文摘要
智能体AI系统在医学影像领域中的应用日益增多，旨在提高处理效率并减轻临床医生的工作负担；然而，安全部署仍然具有挑战性，因为自主错误可能会传播到下游临床决策中。因此，一个核心要求不仅是强大的预测性能，还需要一个可靠的路由机制，以确定系统何时应自主运行，何时应将病例升级以供进一步审查。为解决这一缺口，我们提出了CRC-Router，一个风险约束、不确定性感知的路由模块，适用于传统医学预测模型和智能体医疗AI系统。CRC-Router将多个互补的不确定性信号与预测分数相结合，构建每个发现的路径特征向量，使用轻量级每发现风险模型将该向量映射到估计的错误接受风险，然后应用共形风险控制（CRC）在用户指定的风险目标下校准接受阈值。在NIH ChestX-ray14数据集上对胸部X射线多发现分诊进行实例化，CRC-Router在评估的基线中实现了最强的经验风险-覆盖率权衡，既作为独立路由层，也作为与最先进的MedRAX智能体集成的插件模块。这些结果证明了CRC-Router在选择性医疗自动化中的有效性，以及其模块化、模型无关的兼容性，可与现有的预测和智能体医疗流程集成。代码可从此https URL公开获取。
英文摘要
Agentic AI systems are increasingly being explored in medical imaging to improve throughput and reduce clinician workload; however, safe deployment remains challenging because autonomous errors may propagate into downstream clinical decisions. A central requirement is therefore not only strong predictive performance, but also a reliable routing mechanism that determines when the system should proceed autonomously and when a case should be escalated for further review. To address this gap, we propose CRC-Router, a risk-constrained, uncertainty-aware routing module that is applicable to both conventional medical prediction models and agentic medical AI systems. CRC-Router combines multiple complementary uncertainty signals with the predictive score to construct a per-finding routing feature vector, maps this vector to an estimated wrong-accept risk using a lightweight per-finding risk model, and then applies Conformal Risk Control (CRC) to calibrate acceptance thresholds under a user-specified risk target. Instantiated on chest X-ray multi-finding triage using the NIH ChestX-ray14 dataset, CRC-Router achieves the strongest empirical risk--coverage trade-off among the evaluated baselines, both as a standalone routing layer and as a plug-in module integrated with the state-of-the-art MedRAX agent. These results demonstrate both the effectiveness of CRC-Router in selective medical automation and its modular, model-agnostic compatibility with existing predictive and agentic medical pipelines. Code is publicly available at https://github.com/XLIAaron/CRC-Router
