Mass casualty incidents (MCIs) present major challenges for emergency medical services, overwhelming resources and requiring efficient victim assessment. We introduce ARTEMIS, an AI-driven robotic system using speech and natural language processing, and deep learning for acuity classification, deployed on a mobile quadruped for victim localization and severity assessment. Our simulations and outdoor tests demonstrate that ARTEMIS can achieve triage classification precision exceeding 74% overall and 99% for the most critical cases, significantly outperforming existing deep learning-based systems.