Mass casualty incidents (MCIs) pose a significant challenge to emergency medical services by overwhelming available resources and personnel. Effective victim assessment is the key to minimizing casualties during such a crisis. We introduce ARTEMIS, an AI-driven Robotic Triage Labeling and Emergency Medical Information System, to aid first responders in MCI events. It leverages speech processing, natural language processing, and deep learning to help with acuity classification. This is deployed on a quadruped that performs victim localization and preliminary injury severity assessment. First responders access victim information through a Graphical User Interface that is updated in real-time. To validate our proposed algorithmic triage protocol, we used the Unitree Go1 quadruped. The robot identifies humans, interacts with them, gets vitals and information, and assigns an acuity label. Simulations of an MCI in software and a controlled environment outdoors were conducted. The system achieved a triage-level classification precision of over 74% on average and 99% for the most critical victims, i.e. level 1 acuity, outperforming state-of-the-art deep learning-based triage labeling systems. In this paper, we showcase the potential of human-robot interaction in assisting medical personnel in MCI events.

翻译：大规模伤亡事件对紧急医疗服务构成重大挑战，因其会使可用资源和人员不堪重负。有效的受害者评估是减少此类危机中伤亡的关键。我们提出ARTEMIS——一种AI驱动的机器人分诊标记与紧急医疗信息系统，旨在为大规模伤亡事件中的第一响应者提供辅助。该系统利用语音处理、自然语言处理和深度学习技术辅助病情严重程度分类，并部署于四足机器人上，执行受害者定位及初步伤情评估。第一响应者可通过实时更新的图形用户界面访问受害者信息。为验证所提出的算法分诊协议，我们使用Unitree Go1四足机器人开展实验：机器人识别人类受害者并与之交互，获取生命体征及信息，进而分配严重程度标签。我们在软件中及受控户外环境下进行了大规模伤亡事件模拟。该系统实现了平均超过74%的分诊等级分类精度，对最危急受害者（即1级严重程度）的分类精度达99%，优于当前最先进的基于深度学习的分诊标记系统。本文展示了人机交互在协助医疗人员应对大规模伤亡事件中的潜力。