AI 中文总结
研究针对医院病房物理人工智能系统的安全挑战，提出将临床路径作为安全规范，构建集成多种组件的概念性机器人架构，用运行时安全监视器结合多种方法识别安全违规，助力护理人员并为安全物理人工智能做贡献。
AI 中文摘要
确保在现实环境中运行的物理人工智能系统的安全是一项关键挑战，特别是在医院病房中，脆弱的患者、临床工作人员、医疗设备和辅助机器人并存。在本文中，我们将临床路径重新解释为具体医疗人工智能的明确运行时安全规范。我们提出了一种概念性机器人架构，将可穿戴传感器、智能医疗设备和辅助机器人组件集成到一个统一框架中进行实时安全监控。其核心是一个运行时安全监视器（RSM），根据从规定护理过程中得出的临床定义约束来评估多模态生理和系统级信号。该方法结合时间预测、不确定性感知推理和基于约束的验证来识别安全违规，而不是仅依赖统计异常检测。RSM针对三类事件：与规定护理的生理偏差、硬件和通信故障以及潜在的数据篡改或滥用。这项工作通过将特定领域的临床知识作为可执行的安全约束来实现，弥合基于学习的感知和运行时安全监控之间的差距，以协助现实医院病房中的护理人员，为安全物理人工智能做出了贡献。
英文摘要
Ensuring safety in Physical AI systems operating in real-world environments is a critical challenge, particularly in hospital wards where vulnerable patients, clinical staff, medical devices, and assistive robots coexist. In this paper, we reinterpret Clinical Pathways as explicit runtime safety specifications for embodied medical AI. We propose a conceptual robotic architecture that integrates wearable sensors, smart medical devices, and assistive robotic components into a unified framework for real-time safety monitoring. At its core, a Runtime Safety Monitor (RSM) evaluates multimodal physiological and system-level signals against clinically defined constraints derived from the prescribed care process. Rather than relying solely on statistical anomaly detection, the proposed approach combines temporal prediction, uncertainty-aware reasoning, and constraint-based verification to identify safety violations. The RSM targets three classes of events: physiological deviations from prescribed care, hardware and communication failures, and potential data tampering or misuse. This work contributes to Safe Physical AI by operationalizing domain-specific clinical knowledge as enforceable safety constraints, bridging learning-based perception and runtime safety monitoring to assist nursing staff in real-world hospital wards.
Comments4 pages, 3 figures. Accepted at the 1st IJCAI Workshop on Safe Physical AI (SPAI 2026), held in conjunction with IJCAI-ECAI 2026, Bremen, Germany
