急诊科中基于人工智能的临床决策支持：范围综述

摘要(中文译文)

基于人工智能（AI）的临床决策支持（CDS）有潜力增强急诊科（ED）中的高风险临床决策。然而，其当前的使用情况和向实际应用的转化仍知之甚少。我们提出以下问题：（1）AI-CDS在急诊科个体患者诊疗中的当前应用现状如何？（2）AI-CDS工具已达到哪些开发阶段？我们对AI在急诊科个体患者诊疗中用于预后、诊断和治疗决策的研究进行了范围综述。我们检索了五个数据库（MEDLINE、EMBASE、Cochrane Central、Scopus、Web of Science）以及灰色文献来源，时间范围为2010年1月1日至2023年12月11日。我们遵循了乔安娜布里格斯研究所（Joanna Briggs Institute）的指南和PRISMA范围综述扩展版（PRISMA Extension for Scoping Reviews）。我们在开放科学框架（Open Science Framework）上发布了我们的方案（DOI 10.17605/OSF.IO/FDZ3Y）。在识别出的5168条唯一记录中，我们选取了605项研究纳入综述。大多数（369项，61%）发表于2021至2023年。这些研究涵盖了多种临床应用、患者人群和AI模型类型。预后结局的评估最为常见（270项，44.6%），其次是诊断（193项，31.9%）和处置决策（115项，19%）。大多数研究仍处于临床前开发的最早阶段（572项，94.5%），少数进入后期阶段（33项，5.5%）。通过全面描绘急诊科AI-CDS的应用图景，我们展示了涵盖广泛临床应用的研究数量正在迅速增长，但很少有研究达到高级测试或实施阶段。需要对在急诊科实施AI-CDS的障碍和促进因素进行更细致的了解。

Abstract

Artificial intelligence (AI)-based clinical decision support (CDS) has the potential to augment high-stakes clinical decisions in the emergency department (ED). However, its current usage and translation to implementation remains poorly understood. We asked: (1) What is the current landscape of AI-CDS for individual patient care in the ED? and (2) What phases of development have AI-CDS tools achieved? We performed a scoping review of AI for prognostic, diagnostic, and treatment decisions regarding individual ED patient care. We searched five databases (MEDLINE, EMBASE, Cochrane Central, Scopus, Web of Science) and gray literature sources from January 1, 2010, to December 11, 2023. We adhered to guidelines from the Joanna Briggs Institute and PRISMA Extension for Scoping Reviews. We published our protocol on Open Science Framework (DOI 10.17605/OSF.IO/FDZ3Y). Of 5168 unique records identified, we selected 605 studies for inclusion. The majority (369, 61%) were published in 2021-2023. The studies ranged over a variety of clinical applications, patient populations, and AI model types. Prognostic outcomes were most commonly assessed (270, 44.6%), followed by diagnostic (193, 31.9%) and disposition (115, 19%). Most studies remained in the earliest phase of preclinical development (572, 94.5%) with few advancing to later phases (33, 5.5%). By thoroughly mapping the landscape of AI-CDS in the ED, we demonstrate a rapidly increasing volume of studies covering a breadth of clinical applications, yet few have achieved advanced phases of testing or implementation. A more granular understanding of the barriers and facilitators to implementing AI-CDS in the ED is needed.

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