Clinical Pathways as Safety Specifications for Physical AI in Hospital Wards

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Gabriele Franchini, Giulio Mallardi, Michele De Carolis, Filippo Lanubile
• Venue: arXiv: cs.RO
• Published: 2026-07-22
• Collected: 2026-07-23
• URL: http://arxiv.org/abs/2607.19827v1
• PDF: https://arxiv.org/pdf/2607.19827v1
• DOI: UNKNOWN

Plain-Language Brief

The paper proposes a robotic architecture for Physical AI systems in hospital wards that uses Clinical Pathways as explicit runtime safety specifications. The system, called Runtime Safety Monitor (RSM), evaluates multimodal physiological and system-level signals against clinically defined constraints to ensure safety.

What the Document Says

• The focus is on ensuring safety in Physical AI systems operating in hospital wards.
• Clinical Pathways are used as explicit runtime safety specifications for embodied medical AI.
• A proposed conceptual robotic architecture integrates wearable sensors, smart medical devices, and assistive robotic components.
• The Runtime Safety Monitor (RSM) evaluates multimodal physiological and system-level signals against clinically defined constraints derived from the prescribed care process.
• The RSM 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.
• The goal is to contribute to Safe Physical AI by operationalizing domain-specific clinical knowledge as enforceable safety constraints.

Key Concepts Mentioned

• Physical AI
• hospital wards
• Clinical Pathways
• Runtime Safety Monitor (RSM)
• safety specifications
• wearable sensors
• smart medical devices
• assistive robotic components

Claims Made by the Document

• Using Clinical Pathways as safety specifications can ensure safety in Physical AI systems in hospital wards.
• The proposed RSM can identify safety violations through temporal prediction, uncertainty-aware reasoning, and constraint-based verification.

Evidence Used

• The text describes the proposed approach and its components

Methods / Approach

• temporal prediction
• uncertainty-aware reasoning
• constraint-based verification

Limitations Stated by the Authors

• UNKNOWN (Not specified in the source)

Limitations Noted by the Agent

Agent interpretation — not from the source.

Possible limitations could be the effectiveness and robustness of the RSM under various real-world scenarios or edge cases.

Open Questions

• How effective is the proposed approach in ensuring safety under various real-world hospital ward scenarios?
• What are the potential challenges and solutions for integrating the proposed system into existing hospital infrastructure?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper does not provide specific details about the testing or implementation of the proposed system. Further research could be conducted to understand its practicality.

Raw Abstract / Excerpt

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.