在临床瘤学的基于人工智能的细分模型中基于性别的偏见
F X Doo1, W G Naranjo2, T Kapouranis3
1University 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.
概括
瘤成像中的人工智能 (AI) 必须解决性别特异性的数据,以防止偏见. 确保女性数据的纳入对于在放射性瘤学中公平,公平的AI发展至关重要.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 人工智能增强了临床瘤学成像和放射治疗.
- 人们越来越担心人工智能算法中与性别相关的偏见.
- 在多器官细分开发中,女性特异性数据经常被遗漏.
研究的目的:
- 在基于人工智能的瘤多器官细分中审查生物性别的重要性.
- 在人工智能开发和实施中确定基于性别的偏见的来源.
- 提供建议,以确保人工智能在这个领域的公平.
主要方法:
- 文献评论专注于人工智能,医学成像和瘤学.
- 分析数据生成,模型构建和实施过程.
- 讨论性作为AI算法开发中的变量.
主要成果:
- 生物性别是影响AI细分精度的关键因素.
- 基于性别的偏见可能来自数据收集,算法设计和部署.
- 当前的人工智能模型可能无法在两性之间公平地概括.
结论:
- 解决性别特异性数据对于在瘤学中保持公正的AI至关重要.
- 提高性别包容性是公平和可通用的AI技术所必需的.
- 建议建议促进人工智能在成像指导放射治疗中的公平.
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