分析46个腹部CT数据集，发现严重冗余和地域偏见，影响AI模型临床落地。
State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicability
- 系统评估46个公开腹部CT数据集，发现近六成病例重复使用。
- 超七成数据来自欧美，资源匮乏地区患者严重缺失。
- 提出多中心协作与标准化采集方案，提升AI模型公平性。
本系统综述批判性评估了46个公开的腹部CT数据集（共50,256例）在人工智能临床应用中的适用性。所有数据集中存在显著冗余（病例重复率59.1%），且地理分布严重偏向西方（75.3%来自北美和欧洲）。在包含≥100例的19个数据集中进行偏见评估，发现63%存在领域偏移、57%存在选择偏倚，这些都会削弱模型在多样医疗环境中的泛化能力，尤其在资源有限地区。为此，我们提出针对性改进策略：推动多机构合作、采用标准化采集协议、主动纳入多样化人群与影像技术。这些举措对构建更公平、更具临床鲁棒性的腹部影像AI模型至关重要。
原文摘要 · Abstract (English)
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.
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