Q-VMedSim: A Training Platform for Emergency Response Procedures through Virtual Reality and AI Assistance

Medical training requires hands-on experience with high-risk scenarios, yet traditional simulation methods are costly and logistically challenging. This work-in-progress presents an AI-assisted Virtual Medical Simulator (VMS) designed to enhance emergency procedure training for medical practitioners through immersive VR (Virtual Reality) technology. Built using Unity and C#, the platform replicates a realistic hospital emergency room with high-fidelity 3D models created in Blender and Polycam. The simulation room features interactive components, including a Zoll defibrillator machine, a mannequin on a stretcher, and a sanitization station, providing an authentic environment for practicing emergency interventions. Advanced C# scripting ensures real-time response to user interactions, while RESTful API integration facilitates seamless emergency workflow execution. The platform effectively enhances spatial understanding and equipment familiarity, with users showing significant improvements in operational confidence and procedural accuracy compared to traditional training methods. To enhance procedural training, the system incorporates an AI-powered virtual medical assistant, utilizing OpenAI’s API for context-aware natural language guidance. A MongoDB-backed analytics system tracks user performance, providing real-time insights and adaptive feedback for competency assessment. This research aims to explore the intersection of VR, AI-driven procedural training, and real-time assessment to create a scalable, cost-effective, and immersive learning tool for medical professionals. The project invites discussions on enhancing AI-driven medical training, refining real-time analytics, and expanding cross-platform accessibility. By presenting this work, we seek collaborative insights on optimizing AI interactions, improving procedural workflows, and exploring integrations with existing healthcare training systems.