病棟におけるPhysical AIの安全仕様としての臨床パス
Clinical Pathways as Safety Specifications for Physical AI in Hospital Wards
病棟で動作するPhysical AIの安全性を確保するため、臨床パスを実行時安全仕様として捉え、ウェアラブルセンサや医療機器、支援ロボットを統合した監視アーキテクチャを提案した論文。
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2. 先行研究と比べてどこがすごい？
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著者: Gabriele Franchini, Giulio Mallardi, Michele De Carolis, Filippo Lanubile
分類: cs.RO, cs.CY
原文アブストラクト
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.
