Harvard Medical School, Boston, MA, USA.
Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Faculty of Engineering, University of Porto, Porto, Portugal; Institute for Systems and Computer Engineering, Technology and Science (INESCTEC), Porto, Portugal.
J Biomed Inform. 2024 Apr;152:104631. doi: 10.1016/j.jbi.2024.104631. Epub 2024 Mar 27.
Selection bias can arise through many aspects of a study, including recruitment, inclusion/exclusion criteria, input-level exclusion and outcome-level exclusion, and often reflects the underrepresentation of populations historically disadvantaged in medical research. The effects of selection bias can be further amplified when non-representative samples are used in artificial intelligence (AI) and machine learning (ML) applications to construct clinical algorithms. Building on the "Data Cards" initiative for transparency in AI research, we advocate for the addition of a participant flow diagram for AI studies detailing relevant sociodemographic and/or clinical characteristics of excluded participants across study phases, with the goal of identifying potential algorithmic biases before their clinical implementation. We include both a model for this flow diagram as well as a brief case study explaining how it could be implemented in practice. Through standardized reporting of participant flow diagrams, we aim to better identify potential inequities embedded in AI applications, facilitating more reliable and equitable clinical algorithms.
选择偏倚可能出现在研究的多个方面，包括招募、纳入/排除标准、输入级排除和结果级排除，并且通常反映了在医学研究中历史上处于不利地位的人群代表性不足的问题。当在人工智能 (AI) 和机器学习 (ML) 应用中使用非代表性样本构建临床算法时，选择偏倚的影响会进一步放大。在 AI 研究的“Data Cards”倡议的基础上，我们主张在 AI 研究中添加参与者流程图，详细说明研究各个阶段排除参与者的相关社会人口统计学和/或临床特征，目的是在临床实施之前确定潜在的算法偏差。我们包括此流程图的模型以及一个简短的案例研究，解释如何在实践中实现它。通过标准化报告参与者流程图，我们旨在更好地识别 AI 应用中潜在的不公平现象，从而促进更可靠和公平的临床算法。
