Chee Marcel Lucas, Chee Mark Leonard, Huang Haotian, Mazzochi Katelyn, Taylor Kieran, Wang Han, Feng Mengling, Ho Andrew Fu Wah, Siddiqui Fahad Javaid, Ong Marcus Eng Hock, Liu Nan
Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia.
iScience. 2023 Jul 17;26(8):107407. doi: 10.1016/j.isci.2023.107407. eCollection 2023 Aug 18.
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.
我们的范围综述对人工智能(AI)在院前急救(PEC)中的应用情况进行了全面分析。它通过突出研究最多的人工智能应用,并确定106项纳入研究中最常见的方法学方法,为该领域做出了贡献。研究结果表明,人工智能在院前急救领域有着广阔的前景,有许多独特的用例,如预后预测、需求预测、资源优化以及物联网连续监测系统。与其他方法的比较表明,在大多数情况下,人工智能的表现优于临床医生和非人工智能算法。然而,大多数研究是内部验证且为回顾性的,这凸显了在临床环境中实施人工智能应用之前,需要对其进行严格的前瞻性验证。我们使用证据图谱确定了知识和方法学上的差距,为未来的研究人员提供了路线图。我们还讨论了可解释人工智能对于在临床医生中建立对人工智能系统的信任以及促进人工智能模型的实际应用验证的重要性。