标题： 腹部CT数据集现状：对偏差、临床相关性和现实适用性的批判性综述 显示英文标题

标题： State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicability

摘要： 这项系统综述对公开的腹部CT数据集进行了批判性评估，并分析了它们在临床环境中用于人工智能（AI）应用的适用性。 我们检查了46个公开的腹部CT数据集 （50,256项研究）。 在所有46个数据集中，我们发现存在大量冗余 （59.1% 的病例重复使用）和西方/地理偏倚（75.3% 来自北美和欧洲）。 对包含>=100例病例的19个数据集进行了偏差评估；在该子集中，最普遍的高风险类别是领域偏移（63%）和选择偏倚（57%），这两种情况都可能损害模型在不同医疗环境中的泛化能力——特别是在资源有限的环境中。 为了解决这些挑战，我们提出了针对数据集改进的目标策略，包括多机构合作、采用标准化协议以及有意识地纳入多样化的患者群体和成像技术。 这些努力对于支持开发更公平且临床稳健的腹部影像人工智能模型至关重要。 显示英文摘要

摘要： This systematic review critically evaluates publicly available abdominal CT datasets and their suitability for artificial intelligence (AI) applications in clinical settings. We examined 46 publicly available abdominal CT datasets (50,256 studies). Across all 46 datasets, we found substantial redundancy (59.1\% case reuse) and a Western/geographic skew (75.3\% from North America and Europe). A bias assessment was performed on the 19 datasets with >=100 cases; within this subset, the most prevalent high-risk categories were domain shift (63\%) and selection bias (57\%), both of which may undermine model generalizability across diverse healthcare environments -- particularly in resource-limited settings. To address these challenges, we propose targeted strategies for dataset improvement, including multi-institutional collaboration, adoption of standardized protocols, and deliberate inclusion of diverse patient populations and imaging technologies. These efforts are crucial in supporting the development of more equitable and clinically robust AI models for abdominal imaging.