Masoumian Hosseini Mohsen, Masoumian Hosseini Seyedeh Toktam, Qayumi Karim, Ahmady Soleiman, Koohestani Hamid Reza
Department of E-learning in Medical Sciences, SMART University of Medical Sciences, Tehran, Iran.
Department of Nursing, School of Nursing and Midwifery, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran.
Arch Acad Emerg Med. 2023 May 11;11(1):e38. doi: 10.22037/aaem.v11i1.1974. eCollection 2023.
Artificial Inteligence (AI) application in emergency medicine is subject to ethical and legal inconsistencies. The purposes of this study were to map the extent of AI applications in emergency medicine, to identify ethical issues related to the use of AI, and to propose an ethical framework for its use.
A comprehensive literature collection was compiled through electronic databases/internet search engines (PubMed, Web of Science Platform, MEDLINE, Scopus, Google Scholar/Academia, and ERIC) and reference lists. We considered studies published between 1 January 2014 and 6 October 2022. Articles that did not self-classify as studies of an AI intervention, those that were not relevant to Emergency Departments (EDs), and articles that did not report outcomes or evaluations were excluded. Descriptive and thematic analyses of data extracted from the included articles were conducted.
A total of 137 out of the 2175 citations in the original database were eligible for full-text evaluation. Of these articles, 47 were included in the scoping review and considered for theme extraction. This review covers seven main areas of AI techniques in emergency medicine: Machine Learning (ML) Algorithms (10.64%), prehospital emergency management (12.76%), triage, patient acuity and disposition of patients (19.15%), disease and condition prediction (23.40%), emergency department management (17.03%), the future impact of AI on Emergency Medical Services (EMS) (8.51%), and ethical issues (8.51%).
There has been a rapid increase in AI research in emergency medicine in recent years. Several studies have demonstrated the potential of AI in diverse contexts, particularly when improving patient outcomes through predictive modelling. According to the synthesis of studies in our review, AI-based decision-making lacks transparency. This feature makes AI decision-making opaque.
人工智能（AI）在急诊医学中的应用存在伦理和法律上的不一致性。本研究的目的是梳理AI在急诊医学中的应用范围，识别与AI使用相关的伦理问题，并提出其使用的伦理框架。
通过电子数据库/互联网搜索引擎（PubMed、科学网平台、MEDLINE、Scopus、谷歌学术/学术搜索和教育资源信息中心）及参考文献列表进行全面的文献收集。我们纳入了2014年1月1日至2022年10月6日发表的研究。未自我归类为AI干预研究的文章、与急诊科无关的文章以及未报告结果或评估的文章均被排除。对纳入文章中提取的数据进行描述性和主题分析。
原始数据库中的2175条引用文献中，共有137条符合全文评估标准。其中，47篇文章被纳入范围综述并进行主题提取。本综述涵盖了急诊医学中AI技术的七个主要领域：机器学习（ML）算法（10.64%）、院前急救管理（12.76%）、分诊、患者 acuity及患者处置（19.15%）、疾病和病情预测（23.40%）、急诊科管理（17.03%）、AI对紧急医疗服务（EMS）的未来影响（8.51%）以及伦理问题（8.51%）。
近年来，急诊医学领域的AI研究迅速增加。多项研究已证明AI在不同情境下的潜力，尤其是通过预测建模改善患者预后方面。根据我们综述中的研究综合分析，基于AI的决策缺乏透明度。这一特征使得AI决策不透明。
