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：大规模伤亡事件因资源与人员超负荷，对紧急医疗服务构成重大挑战。在此类危机中，有效的受害者评估是减少伤亡的关键。本文提出ARTEMIS——一种人工智能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急救人员提供支持。该系统利用语音处理、自然语言处理及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与初步伤情评估。急救人员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方案，我们采用Unitree Go1四足机器人进行实验。该机器人能够识别人类、与之交互、获取生命体征与信息，并分配病情严重程度标签。我们在软件环境及受控室外环境中进行了大规模伤亡事件模拟。系统平均分诊级别分类精度超过74%，对最危重受害者（即1级病情）的精度达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。