Ahmed Abdalhalim Abubaker Zakria, Nureldaim Ahmed Sheimaa Nasreldein, Dawoud Ezzelarab Ahmed Mohamed, Mustafa Mawada, Ali Albasheer Mohammed Gabralla, Abdelgadir Ahmed Roaa Eltag, Galal Eldin Elsayed Mowafag Bushra
Emergency Medicine, Rustaq Hospital, Rustaq, OMN.
General Medicine, Sheikh Khalifa General Hospital, Ummalquwain, ARE.
Cureus. 2025 Jun 9;17(6):e85667. doi: 10.7759/cureus.85667. eCollection 2025 Jun.
Emergency departments (EDs) worldwide face increasing pressure to optimize triage processes amidst rising patient volumes and resource constraints. Artificial intelligence (AI) has emerged as a potential solution to enhance triage accuracy and efficiency, yet its real-world clinical impact remains inadequately characterized. We conducted a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, searching PubMed/Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (Embase), Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) Xplore (2020-2025) for studies evaluating AI-based ED triage systems. From 119 initially identified records, six studies met inclusion criteria after duplicate removal (n=67), title/abstract screening (n=52), and full-text assessment (n=12). Eligible studies reported quantitative outcomes on AI performance compared to traditional triage methods. Risk of bias was assessed using an adapted Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Narrative synthesis was employed due to methodological heterogeneity. The included studies (n=6) demonstrated AI's potential to reduce triage time, improve documentation accuracy, and enhance decision support. Voice-based artificial intelligence (Voice-AI) systems achieved 19% faster documentation versus manual methods, while machine learning algorithms reduced mis-triage rates by 0.3-8.9%. However, limitations included undertriage risks, variable accuracy, and predominance of single-center studies. Implementation challenges encompassed workflow integration barriers and insufficient clinician acceptance metrics. AI-based triage systems show promise for improving ED efficiency but require rigorous multi-center validation and standardized outcome reporting. Key gaps include evidence on patient-centered outcomes, equity considerations, and long-term impact studies. Future development should prioritize explainable algorithms, clinician engagement, and ethical frameworks to ensure safe implementation.
在全球范围内，急诊科（EDs）面临着越来越大的压力，需要在患者数量不断增加和资源受限的情况下优化分诊流程。人工智能（AI）已成为提高分诊准确性和效率的潜在解决方案，但其在现实世界中的临床影响仍未得到充分描述。我们按照系统评价和Meta分析的首选报告项目（PRISMA）2020指南进行了一项系统评价，在PubMed/医学文献分析和在线检索系统（MEDLINE）、医学文摘数据库（Embase）、科学网和电气与电子工程师协会（IEEE）Xplore（2020 - 2025年）中搜索评估基于人工智能的急诊科分诊系统的研究。从最初识别的119条记录中，经过重复记录去除（n = 67）、标题/摘要筛选（n = 52）和全文评估（n = 12）后，有6项研究符合纳入标准。符合条件的研究报告了与传统分诊方法相比人工智能性能的定量结果。使用改编后的诊断准确性研究质量评估工具2（QUADAS - 2）评估偏倚风险。由于方法学的异质性，采用了叙述性综合分析。纳入的研究（n = 6）证明了人工智能在减少分诊时间、提高文档准确性和增强决策支持方面的潜力。基于语音的人工智能（Voice - AI）系统与手动方法相比，文档记录速度快19%，而机器学习算法将误分诊率降低了0.3 - 8.9%。然而，局限性包括分诊不足风险、准确性差异以及单中心研究占主导地位。实施挑战包括工作流程整合障碍和临床医生接受度指标不足。基于人工智能的分诊系统有望提高急诊科效率，但需要严格的多中心验证和标准化的结果报告。关键差距包括以患者为中心的结果、公平性考虑和长期影响研究方面的证据。未来的发展应优先考虑可解释的算法、临床医生参与和道德框架，以确保安全实施。
