Cold Kristoffer Mazanti, Vamadevan Anishan, Laursen Christian B, Bjerrum Flemming, Singh Suveer, Konge Lars
Copenhagen Academy for Medical Education and Simulation (CAMES), Center for HR & Education, The Capital Region of Denmark, Copenhagen, Denmark
Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Eur Respir Rev. 2025 May 28;34(176). doi: 10.1183/16000617.0274-2024. Print 2025 Apr.
Artificial intelligence (AI) systems have been implemented to improve the diagnostic yield and operators' skills within endoscopy. Similar AI systems are now emerging in bronchoscopy. Our objective was to identify and describe AI systems in bronchoscopy.
A systematic review was performed using MEDLINE, Embase and Scopus databases, focusing on two terms: bronchoscopy and AI. All studies had to evaluate their AI against human ratings. The methodological quality of each study was assessed using the Medical Education Research Study Quality Instrument (MERSQI).
1196 studies were identified, with 20 passing the eligibility criteria. The studies could be divided into three categories: nine studies in airway anatomy and navigation, seven studies in computer-aided detection and classification of nodules in endobronchial ultrasound, and four studies in rapid on-site evaluation. 16 were assessment studies, with 12 showing equal performance and four showing superior performance of AI compared with human ratings. Four studies within airway anatomy implemented their AI, all favouring AI guidance to no AI guidance. The methodological quality of the studies was moderate (mean MERSQI 12.9 points, out of a maximum 18 points).
20 studies developed AI systems, with only four examining the implementation of their AI. The four studies were all within airway navigation and favoured AI to no AI in a simulated setting. Future implementation studies are warranted to test for the clinical effect of AI systems within bronchoscopy.
人工智能（AI）系统已被应用于提高内镜检查的诊断率和操作人员的技能。类似的人工智能系统目前也正在支气管镜检查中出现。我们的目的是识别和描述支气管镜检查中的人工智能系统。
使用MEDLINE、Embase和Scopus数据库进行系统评价，重点关注两个术语：支气管镜检查和人工智能。所有研究都必须将其人工智能与人类评级进行比较评估。使用医学教育研究质量工具（MERSQI）评估每项研究的方法学质量。
共识别出1196项研究，其中20项通过了纳入标准。这些研究可分为三类：9项关于气道解剖和导航的研究，7项关于支气管内超声中结节的计算机辅助检测和分类的研究，以及4项关于快速现场评估的研究。16项为评估研究，其中12项显示人工智能与人类评级表现相当，4项显示人工智能表现优于人类评级。气道解剖领域的4项研究应用了其人工智能，均支持人工智能引导而非无人工智能引导。这些研究的方法学质量中等（平均MERSQI为12.9分，满分18分）。
20项研究开发了人工智能系统，只有4项研究考察了其人工智能的应用情况。这4项研究均属于气道导航领域，且在模拟环境中支持人工智能引导而非无人工智能引导。未来有必要开展应用研究，以测试人工智能系统在支气管镜检查中的临床效果。
