Hazard Management in Robot-Assisted Mammography Support

TL;DR

This paper introduces a hazard management methodology for a robotic mammography support system, combining stakeholder input with formal safety analysis techniques to identify and mitigate safety risks in clinical use.

Contribution

It presents a structured safety-driven design approach integrating stakeholder collaboration, SHARD, and STPA for assistive robots in healthcare settings.

Findings

Hazards often stem from timing mismatches and misinterpretations, not component failures.

Safety requirements were refined to mitigate identified hazards and reduce reliance on human timing.

The methodology is applicable to other assistive robotic systems in clinical environments.

Abstract

Robotic and embodied-AI systems have the potential to improve accessibility and quality of care in clinical settings, but their deployment in close physical contact with vulnerable patients introduces significant safety risks. This paper presents a hazard management methodology for MammoBot, an assistive robotic system designed to support patients during X-ray mammography. To ensure safety from early development stages, we combine stakeholder-guided process modelling with Software Hazard Analysis and Resolution in Design (SHARD) and System-Theoretic Process Analysis (STPA). The robot-assisted workflow is defined collaboratively with clinicians, roboticists, and patient representatives to capture key human-robot interactions. SHARD is applied to identify technical and procedural deviations, while STPA is used to analyse unsafe control actions arising from user interaction. The results show that many hazards arise not from component failures, but from timing mismatches, premature actions, and misinterpretation of system state. These hazards are translated into refined and additional safety requirements that constrain system behaviour and reduce reliance on correct human timing or interpretation alone. The work demonstrates a structured and traceable approach to safety-driven design with potential applicability to assistive robotic systems in clinical environments.