Clinical Pathways as Safety Specifications for Physical AI in Hospital Wards

2026-07-22

AI Summary

This work addresses the lack of enforceable safety mechanisms in embodied medical AI systems deployed in hospital wards by proposing a novel approach that formalizes clinical pathways into runtime safety constraints. The authors develop a unified architecture integrating wearable sensors, smart devices, and assistive robots, which leverages multimodal signal fusion, temporal prediction, uncertainty-aware reasoning, and constraint satisfaction verification to enable real-time detection and proactive intervention for three critical risks: physiological anomalies, hardware failures, and data tampering. By translating clinical pathways—typically high-level procedural guidelines—into executable safety specifications, this method bridges the gap between learning-based perception and stringent clinical safety requirements, thereby substantially enhancing the reliability and trustworthiness of AI systems operating in dynamic ward environments.

Abstract

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.

Problem

• Physical AI
• Clinical Pathways
• Runtime Safety
• Hospital Wards
• Safety Specifications

Innovation

• Clinical Pathways
• Runtime Safety Monitor
• Physical AI
• Constraint-based Verification
• Multimodal Safety Monitoring