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.

翻译：机器人具身AI系统有潜力改善临床环境的可及性和护理质量，但与脆弱患者的密切物理接触会引入重大安全风险。本文提出了一种针对MammoBot的危害管理方法，这是一个设计用于在X射线乳腺摄影过程中为患者提供支持的辅助机器人系统。为确保从早期开发阶段便实现安全性，我们将利益相关者引导的流程建模与软件危害分析与解决设计（SHARD）及系统理论过程分析（STPA）相结合。与临床医生、机器人专家和患者代表协作定义了机器人辅助工作流程，以捕获关键的人机交互。应用SHARD识别技术和程序偏差，同时使用STPA分析由用户交互引起的不安全控制行为。结果表明，许多危害并非源于组件故障，而是来自时间错配、过早动作以及对系统状态的误解。这些危害被转化为改进的附加安全要求，以约束系统行为并减少对人类正确时机判断或解释的依赖。该工作展示了一种结构化且可追溯的安全驱动设计方法，对临床环境中的辅助机器人系统具有潜在适用性。