Farrokhi Mehrdad, Fallahian Amir H, Rahmani Erfan, Aghajan Ali, Alipour Morteza, Jafari Khouzani Parisa, Boustani Hezarani Hossein, Sabzehie Hamed, Pirouzan Mohammad, Pirouzan Zahra, Dalvandi Behnaz, Dalvandi Reza, Doroudgar Parisa, Azimi Habib, Moradi Fatemeh, Nozari Amitis, Sharifi Maryam, Ghorbani Hamed, Moghimi Sara, Azarkish Fatemeh, Bolandi Soheil, Esfahani Hooman, Hosseinmirzaei Sara, Niknam Arezou, Nikfarjam Farzaneh, Talebi Boroujeni Parham, Noorbakhsh Mahyar, Rahmani Parham, Rostamian Motlagh Fatemeh, Harati Khadijeh, Farrokhi Masoud, Talebi Sina, Zare Lahijan Lida
Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
ERIS Research Institute, Tehran, Iran.
Arch Acad Emerg Med. 2025 Apr 15;13(1):e45. doi: 10.22037/aaemj.v13i1.2712. eCollection 2025.
Artificial intelligence (AI) systems have witnessed notable advancements, revolutionizing various fields of research and medicine. Specifically, advancements of AI and the rapid growth of machine learning hold immense potential to significantly impact emergency medicine. This narrative review aimed to summarize AI applications in prehospital emergency care, emergency radiology, triage and patient classification, emergency diagnosis and interventions, pediatric emergency care, trauma care, outcome prediction, as well as the legal and ethical challenges and limitations of AI use in emergency medicine. A comprehensive literature search was conducted in Web of Science, Scopus, and Medline using a wide range of artificial intelligence and machine learning-related keywords combined with terms related to emergency medicine to identify relevant published studies. The findings show that AI-powered tools can assist clinicians in emergency departments in improving the management of prehospital emergency care, emergency radiology, triage, emergency department workflow, complex diagnoses, treatment, clinical decision-making, pediatric emergency care, trauma care, and the prediction of admissions, discharges, complications, and outcomes. However, the majority of these applications have been reported in retrospective studies, whereas randomized controlled trials (RCTs) are essential to determine the true value of AI in emergency settings. These applications can serve as effective tools in emergency departments when they are continuously supplied with high-quality real-time data and are adopted through collaboration between skilled data scientists and clinicians. Implementing these AI-assisted tools in emergency departments requires adequate infrastructure and machine learning operation systems. Since emergency medicine involves various clinical decision-making scenarios based on classifications, flowcharts, and well-structured approaches, future well-designed prospective studies are necessary to achieve the goal of replacing conventional methods with new AI and machine learning techniques.
人工智能（AI）系统取得了显著进展，给各个研究和医学领域带来了变革。具体而言，人工智能的进步和机器学习的快速发展在显著影响急诊医学方面具有巨大潜力。本叙述性综述旨在总结人工智能在院前急救、急诊放射学、分诊与患者分类、急诊诊断与干预、儿科急诊护理、创伤护理、结局预测，以及人工智能在急诊医学应用中的法律和伦理挑战与局限性等方面的应用。在科学网、Scopus和Medline数据库中进行了全面的文献检索，使用了一系列与人工智能和机器学习相关的关键词，并结合与急诊医学相关的术语来识别相关的已发表研究。研究结果表明，人工智能驱动的工具可以帮助急诊科的临床医生改善院前急救、急诊放射学、分诊、急诊科工作流程、复杂诊断、治疗、临床决策、儿科急诊护理、创伤护理，以及入院、出院、并发症和结局预测等方面的管理。然而，这些应用大多在回顾性研究中有所报道，而随机对照试验（RCT）对于确定人工智能在急诊环境中的真正价值至关重要。当这些应用持续获得高质量实时数据，并通过熟练的数据科学家和临床医生之间的合作来采用时，它们可以成为急诊科的有效工具。在急诊科实施这些人工智能辅助工具需要适当的基础设施和机器学习操作系统。由于急诊医学涉及基于分类、流程图和结构良好的方法的各种临床决策场景，未来需要精心设计前瞻性研究，以实现用新的人工智能和机器学习技术取代传统方法的目标。
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