临床肿瘤学中基于人工智能的分割模型中的性别偏见
Sex-Based Bias in Artificial Intelligence-Based Segmentation Models in Clinical Oncology.
作者信息
Doo F X, Naranjo W G, Kapouranis T, Thor M, Chao M, Yang X, Marshall D C
机构信息
University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD, USA; University of Maryland-Institute for Health Computing (UM-IHC), University of Maryland, North Bethesda, MD, USA.
Department of Medical Physics, Columbia University, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
出版信息
Clin Oncol (R Coll Radiol). 2025 Mar;39:103758. doi: 10.1016/j.clon.2025.103758. Epub 2025 Jan 8.
Artificial intelligence (AI) advancements have accelerated applications of imaging in clinical oncology, especially in revolutionizing the safe and accurate delivery of state-of-the-art imaging-guided radiotherapy techniques. However, concerns are growing over the potential for sex-related bias and the omission of female-specific data in multi-organ segmentation algorithm development pipelines. Opportunities exist for addressing sex-specific data as a source of bias, and improving sex inclusion to adequately inform the development of AI-based technologies to ensure their fairness, generalizability and equitable distribution. The goal of this review is to discuss the importance of biological sex for AI-based multi-organ image segmentation in routine clinical and radiation oncology; sources of sex-based bias in data generation, model building and implementation and recommendations to ensure AI equity in this rapidly evolving domain.
人工智能（AI）的进步加速了成像技术在临床肿瘤学中的应用，特别是在革新安全、准确地实施先进的成像引导放射治疗技术方面。然而，人们越来越担心在多器官分割算法开发流程中存在与性别相关的偏见以及遗漏女性特定数据的可能性。将特定性别的数据作为偏见来源加以解决，并提高性别包容性，以便为基于人工智能的技术开发提供充分信息，从而确保其公平性、通用性和公平分配，这是存在机会的。本综述的目的是讨论生物性别对于常规临床和放射肿瘤学中基于人工智能的多器官图像分割的重要性；数据生成、模型构建和实施过程中基于性别的偏见来源，以及在这个快速发展的领域确保人工智能公平性的建议。
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