AI辅助超声在创伤中的应用：一项范围综述。
完整摘要 · 中文译文
目的：人工智能（AI）已成为医学领域的一项颠覆性技术，其在教育、诊断、图像增强与评估、预后及治疗方面的应用意义正在迅速发展。关于AI辅助超声的文献仍处于发展阶段。因此，我们旨在探讨AI如何在创伤超声检查中被应用，以及AI辅助超声如何在临床实践中得到部署。 材料与方法：本综述按照JBI范围审查方法进行。最初为PubMed、Scopus、Web of Science（WoS）和Google Scholar（后向及前向引文追踪）制定了适当的检索策略。使用各数据库相应的医学主题词（MeSH）对“人工智能”、“超声”和“创伤”的概念进行了检索。核心概念为AI应用与超声在创伤中的结合。在此背景下，本综述纳入了以下与AI辅助超声在创伤领域相关的类别的研究：(1) 诊断准确性，(2) 鉴别诊断及诊断确认，(3) 质量与标准化，(4) 操作者支持，以及 (5) 培训与建模。数据提取和分析使用Microsoft Excel进行，并经过验证。 结果：共纳入21项研究。靶向AI任务主要包括诊断性异常检测和分割/定位。主要侧重于创伤超声重点评估（FAST）、胸部创伤评估及肢体创伤。 尽管在开发阶段的回顾性队列中报告了较高的表观诊断准确性，但仅有的一项研究执行了独立的外部验证，其余研究受限于单中心数据或合成体模。至关重要的是，没有一项研究评估了后续的患者结局，如死亡率、干预时间或资源利用情况。 结论：本范围综述证实，尽管深度学习框架在回顾性诊断性能和图像质量自动化方面显示出有前景的表现，但其向急性创伤护理的转化面临着关键的技术和临床瓶颈。当前的主要局限性包括模型可解释性受限、缺乏监管途径、临床医生信任不足、固有的决策偏倚、算法透明度差，以及缺乏患者结局追踪。为应对这些障碍，未来的研究应以更强的方法学严谨性进行设计，包括前瞻性、多中心、随机对照及外部验证的方法。 试验注册：无可用临床试验注册号。
完整摘要 · 英文原文
OBJECTIVE: Artificial intelligence (AI) has become a disruptive technology in medicine, with rapidly developing implications for education, diagnosis, image enhancement and assessment, prognosis, and treatment. The literature on AI-assisted ultrasound is still developing. Therefore, we aimed to investigate how AI is employed in ultrasound for trauma and how AI-assisted ultrasound could be deployed in clinical practice. MATERIALS AND METHODS: The review was conducted according to JBI's scoping review methodology. An appropriate search strategy was initially formulated for PubMed, Scopus, Web of Science (WoS), and Google Scholar (backward and forward citation tracking). The concepts of "artificial intelligence", "ultrasound", and "trauma" were searched across these databases using their respective Medical Subject Headings (MeSH). The core concepts were AI applications integrated with ultrasound in trauma. In this context, studies focusing on the following categories relating to the use of AI-assisted ultrasound in the field of trauma have been included in the review: (1) diagnostic accuracy, (2) differential diagnosis and confirmation of diagnosis, (3) quality and standardization, (4) operator support, and (5) training and modelling. Data extraction and analysis were performed using Microsoft Excel and were verified. RESULTS: Twenty-one studies were included. Targeted AI tasks primarily comprised diagnostic abnormality detection and segmentation/localization. Predominantly focused assessment with sonography for trauma (FAST), thoracic trauma evaluation, and extremity trauma. Although high apparent diagnostic accuracy was reported across development-stage retrospective cohorts, independent external validation was executed in only one study, with the remainder bounded by single-center data or synthetic phantoms. Crucially, none of the studies assessed subsequent patient outcomes such as mortality, time to intervention, or resource utilization. CONCLUSION: This scoping review confirms that while deep learning frameworks show promising retrospective diagnostic performance and image-quality automation, their translation into acute trauma care faces critical technical and clinical bottlenecks. Key current limitations include restricted model interpretability, missing regulatory pathways, a lack of clinician trust, inherent decision-making biases, poor algorithmic transparency, and the lack of patient-outcome tracking. To address these barriers, future studies should be designed with stronger methodological rigor, including prospective, multicenter, randomized controlled, and externally validated approaches. TRIAL REGISTRATION: No clinical trial number is available.