PDF(Pubmed)
Abstract：
BACKGROUND: Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM.
OBJECTIVE: Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs\' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field.
METHODS: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs\' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data.
RESULTS: A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs\' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs\' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills.
CONCLUSIONS: LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians\' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.
OBJECTIVE: Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs\' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field.
METHODS: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs\' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data.
RESULTS: A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs\' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs\' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills.
CONCLUSIONS: LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians\' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.
摘要：
背景：人工智能（AI），更具体地说，大型语言模型(LLM)，通过优化临床工作流程和提高决策质量，在彻底改变急诊护理提供方面具有巨大潜力。尽管将LLM整合到急诊医学（EM）中的热情正在增长，现有文献的特点是不同的个体研究集合，概念分析,和初步实施。鉴于这些复杂性和理解上的差距，需要一个有凝聚力的框架来理解现有的关于在EM中应用LLM的知识体系。
目标：鉴于缺乏全面的框架来探索LLM在EM中的作用，本范围审查旨在系统地绘制有关EM中LLM的潜在应用的现有文献，并确定未来研究的方向。解决这一差距将有助于在实地取得知情进展。
方法：使用PRISMA-ScR（系统审查的首选报告项目和范围审查的荟萃分析扩展）标准，我们搜索了OvidMEDLINE,Embase,WebofScience,和谷歌学者在2018年1月至2023年8月之间发表的论文中讨论了LLM在EM中的使用。我们排除了其他形式的AI。总共筛选了1994年的独特标题和摘要，每篇全文由2名作者独立审查。数据是独立提取的，5位作者对数据进行了定量和定性的协同合成。
结果：共纳入43篇论文。研究主要从2022年到2023年，在美国和中国进行。我们发现了四个主要主题：（1）临床决策和支持被强调为关键领域，LLM在加强患者护理方面发挥着重要作用，特别是通过它们在实时分诊中的应用，允许早期识别患者的紧迫性；(2)效率，工作流,和信息管理证明了LLM显著提高运营效率的能力，特别是通过病人记录合成的自动化，这可以减轻行政负担，加强以患者为中心的护理；(3)风险，伦理,透明度被确定为关注领域，特别是关于LLM输出的可靠性，具体研究强调了在潜在有缺陷的训练数据集中确保无偏见决策的挑战，强调彻底验证和道德监督的重要性；（4）教育和沟通的可能性包括法学硕士丰富医学培训的能力，例如通过使用增强沟通技巧的模拟患者互动。
结论：LLM有可能从根本上改变EM，加强临床决策，优化工作流，改善患者预后。这篇综述通过确定关键研究领域为未来的进步奠定了基础：LLM应用的前瞻性验证，建立负责任使用的标准，理解提供者和患者的看法，提高医生的人工智能素养。有效地将LLM集成到EM中需要协作努力和全面评估，以确保这些技术能够安全有效地应用。
目标：鉴于缺乏全面的框架来探索LLM在EM中的作用，本范围审查旨在系统地绘制有关EM中LLM的潜在应用的现有文献，并确定未来研究的方向。解决这一差距将有助于在实地取得知情进展。
方法：使用PRISMA-ScR（系统审查的首选报告项目和范围审查的荟萃分析扩展）标准，我们搜索了OvidMEDLINE,Embase,WebofScience,和谷歌学者在2018年1月至2023年8月之间发表的论文中讨论了LLM在EM中的使用。我们排除了其他形式的AI。总共筛选了1994年的独特标题和摘要，每篇全文由2名作者独立审查。数据是独立提取的，5位作者对数据进行了定量和定性的协同合成。
结果：共纳入43篇论文。研究主要从2022年到2023年，在美国和中国进行。我们发现了四个主要主题：（1）临床决策和支持被强调为关键领域，LLM在加强患者护理方面发挥着重要作用，特别是通过它们在实时分诊中的应用，允许早期识别患者的紧迫性；(2)效率，工作流,和信息管理证明了LLM显著提高运营效率的能力，特别是通过病人记录合成的自动化，这可以减轻行政负担，加强以患者为中心的护理；(3)风险，伦理,透明度被确定为关注领域，特别是关于LLM输出的可靠性，具体研究强调了在潜在有缺陷的训练数据集中确保无偏见决策的挑战，强调彻底验证和道德监督的重要性；（4）教育和沟通的可能性包括法学硕士丰富医学培训的能力，例如通过使用增强沟通技巧的模拟患者互动。
结论：LLM有可能从根本上改变EM，加强临床决策，优化工作流，改善患者预后。这篇综述通过确定关键研究领域为未来的进步奠定了基础：LLM应用的前瞻性验证，建立负责任使用的标准，理解提供者和患者的看法，提高医生的人工智能素养。有效地将LLM集成到EM中需要协作努力和全面评估，以确保这些技术能够安全有效地应用。
