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This paper reinterprets Clinical Pathways as runtime safety specifications for Physical AI systems in hospital wards, addressing the critical challenge of ensuring safety in environments with vulnerable patients and clinical staff. The authors propose a conceptual robotic architecture that integrates wearable sensors and smart medical devices, utilizing a Runtime Safety Monitor (RSM) to evaluate multimodal signals against clinically defined constraints. Key findings reveal that the RSM effectively identifies safety violations through a combination of temporal prediction, uncertainty-aware reasoning, and constraint-based verification, enhancing the operational safety of embodied medical AI in real-world settings.
Reimagining Clinical Pathways as enforceable safety constraints could revolutionize how Physical AI systems operate in sensitive hospital environments.
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.