Conference proceeding
System Development and Evaluation for Mass Casualty Incidents Triage with Virtual Reality and Artificial Intelligence
Proceedings of the International ISCRAM Conference
22nd International ISCRAM Conference: Managing and responding to coastal disasters and climate change (Halifax, NS, Canada, 18/05/2025–21/05/2025)
05/2025
Abstract
This study investigates the integration of Virtual Reality (VR) and Artificial Intelligence (AI) to enhance pre-hospital triage training for Mass Casualty Incidents (MCIs). Traditional training methods, such as field drills and full-scale simulations, are often costly and logistically challenging, while simpler methods like tabletop exercises remain limited in realism. To address these limitations, a VR learning tool was developed to simulate realistic emergency scenarios, providing emergency healthcare professionals with an immersive and cost-effective training environment to refine triage skills. The VR learning tool records both VR sensor data and speech data, and then utilizes statistical and AI methods (such as automatic speech recognition, and natural language processing) to process these data for evaluation. The survey results showed that participants with varying levels of experience found the VR training highly immersive and engaging. Additionally, AI-driven analysis of speech data from the training demonstrated improved consistency and correctness in participants’ communication over time. This research demonstrates VR’s potential as a valuable supplement to traditional training, identifying key areas for future development.
Details
- Title
- System Development and Evaluation for Mass Casualty Incidents Triage with Virtual Reality and Artificial Intelligence
- Authors/Creators
- David ParryPeng XiaJi RuanStephen AielloJian YuSally Britnell
- Publication Details
- Proceedings of the International ISCRAM Conference
- Conference
- 22nd International ISCRAM Conference: Managing and responding to coastal disasters and climate change (Halifax, NS, Canada, 18/05/2025–21/05/2025)
- Identifiers
- 991005812020507891
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Conference proceeding
Metrics
2 File views/ downloads
17 Record Views