GEMMA3N: Enhanced AI Emergency Response System

Abstract

Emergency response systems globally face challenges of triage and dispatch delays and a lack of en-route medical advice. These issues are due to manual decision making, fragmented information flows, and the lack of systems that can predict the severity of an emergency. In the era of AI-powered models, there is potential to augment traditional emergency response workflows with models to design them to be proactive and adaptive. In this work, we survey the existing works of AI-powered triage, deep learning powered medical evaluation, intelligent dispatching, and automated resource allocation and present a unified system called GEMMA3N, the Enhanced AI Emergency Response System, which can help to fill the gap from the detection to a timely response. Our system provides a unified pipeline that brings together ML-driven severity classification, automated dispatching, LLM-guided first-aid communication, and optimized ambulance navigation. In this process, we collate research from emergency medicine, model development, and real time decision automation to reduce response time, empower medical teams with actionable insights, and provide immediate assistance in critical situations. We discuss our design choices, implementation considerations, observations, and lessons learned in our experience to deploy AI models on real world emergency systems.