Tsai, Chu-LinChu-LinTsaiChu, Teresa Cheng-ChiehTeresa Cheng-ChiehChuWang, Chih-HungChih-HungWangWang, Kuo-ChuanKuo-ChuanWangChang, Wei-TienWei-TienChangLiu, Wei-LunWei-LunLiuKu, Shih-ChiShih-ChiKuHAO-CHIH TAISHUENN-WEN KUOTsai, Min-ShanMin-ShanTsaiChao, AnneAnneChaoTang, Sung-ChunSung-ChunTangTsai, Ming-HanMing-HanTsaiWang, Ting-AnnTing-AnnWangChuang, Shu-LinShu-LinChuangLin, Yen-HungYen-HungLinChen, Chiuan-JungChiuan-JungChenLee, Yi-ChiaYi-ChiaLeeKuo, Lu-ChengLu-ChengKuoKao, Jia-HorngJia-HorngKaoWang, WeichungWeichungWangHuang, Chien-HuaChien-HuaHuang2025-09-122025-09-122025-08-20https://scholars.lib.ntu.edu.tw/handle/123456789/732024Background: Advancements in artificial intelligence (AI) have driven substantial breakthroughs in computer-aided detection (CAD) for chest x-ray (CXR) imaging. The National Taiwan University Hospital research team previously developed an AI-based emergency CXR system (Capstone project), which led to the creation of a CXR module. This CXR module has an established model supported by extensive research and is ready for application in clinical trials without requiring additional model training. This study will use 3 submodules of the system: detection of misplaced endotracheal tubes, detection of misplaced nasogastric tubes, and identification of pneumothorax. Objective: This study aims to apply a real-time CXR CAD system in emergency and critical care settings to evaluate its clinical and economic benefits without requiring additional CXR examinations or altering standard care and procedures. The study will evaluate the impact of CAD system on mortality reduction, postintubation complications, hospital stay duration, workload, and interpretation time, as wells as conduct a cost-effectiveness comparison with standard care. Methods: This study adopts a pilot trial and cluster randomized controlled trial design, with random assignment conducted at the ward level. In the intervention group, units are granted access to AI diagnostic results, while the control group continues standard care practices. Consent will be obtained from attending physicians, residents, and advanced practice nurses in each participating ward. Once consent is secured, these health care providers in the intervention group will be authorized to use the CAD system. Intervention units will have access to AI-generated interpretations, whereas control units will maintain routine medical procedures without access to the AI diagnostic outputs. Results: The study was funded in September 2024. Data collection is expected to last from January 2026 to December 2027.enartificial intelligenceclinical effectivenesscomputer-aided detection systemcost-effectivenessendotracheal tubenasogastric tubepneumothorax diagnosis[SDGs]SDG3journal article10.2196/7292840834403