Artificial intelligence and machine learning in prehospital emergency care: A scoping review
Marcel Lucas Chee, Mark Leonard Chee, Haotian Huang, Katelyn Mazzochi, Kieran Taylor, Han Wang, Mengling Feng, Andrew Fu Wah Ho, Fahad Javaid Siddiqui, Marcus Eng Hock Ong, Nan Liu
Abstract
Our scoping review provides a comprehensive analysis of the landscape of artificial intelligence (AI) applications in prehospital emergency care (PEC). It contributes to the field by highlighting the most studied AI applications and identifying the most common methodological approaches across 106 included studies. The findings indicate a promising future for AI in PEC, with many unique use cases, such as prognostication, demand prediction, resource optimization, and the Internet of Things continuous monitoring systems. Comparisons with other approaches showed AI outperforming clinicians and non-AI algorithms in most cases. However, most studies were internally validated and retrospective, highlighting the need for rigorous prospective validation of AI applications before implementation in clinical settings. We identified knowledge and methodological gaps using an evidence map, offering a roadmap for future investigators. We also discussed the significance of explainable AI for establishing trust in AI systems among clinicians and facilitating real-world validation of AI models.