Medical imaging datasets are fundamental to artificial intelligence (AI) in healthcare. The accuracy, robustness and fairness of diagnostic algorithms depend on the data (and its quality) on which the models are trained and evaluated. Medical imaging datasets have become increasingly available to the public, and are often hosted on Community-Contributed Platforms (CCP), including private companies like Kaggle or HuggingFace. While open data is important to enhance the redistribution of data's public value, we find that the current CCP governance model fails to uphold the quality needed and recommended practices for sharing, documenting, and evaluating datasets. In this paper we investigate medical imaging datasets on CCPs and how they are documented, shared, and maintained. We first highlight some differences between medical imaging and computer vision, particularly in the potentially harmful downstream effects due to poor adoption of recommended dataset management practices. We then analyze 20 (10 medical and 10 computer vision) popular datasets on CCPs and find vague licenses, lack of persistent identifiers and storage, duplicates and missing metadata, with differences between the platforms. We present "actionability" as a conceptual metric to reveal the data quality gap between characteristics of data on CCPs and the desired characteristics of data for AI in healthcare. Finally, we propose a commons-based stewardship model for documenting, sharing and maintaining datasets on CCPs and end with a discussion of limitations and open questions.

翻译：医学影像数据集是医疗人工智能（AI）的基础。诊断算法的准确性、鲁棒性和公平性取决于模型训练和评估所使用的数据（及其质量）。医学影像数据集已日益向公众开放，并常托管于社区贡献平台（CCP），包括Kaggle或HuggingFace等私营企业。尽管开放数据对增强数据公共价值的再分配至关重要，但我们发现当前CCP治理模式未能维护共享、记录和评估数据集所需的质量及推荐实践。本文研究了CCP上的医学影像数据集及其记录、共享和维护方式。我们首先强调医学影像与计算机视觉之间的差异，特别是在因未遵循推荐的数据集管理实践而可能引发的有害下游效应方面。随后分析了CCP上20个热门数据集（10个医学领域和10个计算机视觉领域），发现存在许可证模糊、缺乏持久标识符和存储、重复及元数据缺失等问题，且不同平台间表现各异。我们提出“可操作性”作为概念性指标，以揭示CCP上数据特征与医疗AI所需数据特征之间的质量差距。最后，我们提出基于共同体的治理模型，用于在CCP上记录、共享和维护数据集，并以局限性和开放性问题的讨论作结。