了解放射学方面的偏见和差异人工智能数据集:一篇评论
Satvik Tripathi1, Kyla Gabriel2, Suhani Dheer1
1Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania.
Journal of the American College of Radiology : JACR
|July 16, 2023
概括
高质量,多样化的医学成像数据集对于开发精确的人工智能 (AI) 疾病检测模型至关重要. 目前的公共数据集缺乏包容性,阻碍了人工智能.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 生物医学数据科学是生物医学数据科学.
背景情况:
- 人工智能 (AI) 显示出在医疗成像中提高疾病检测和诊断准确性的巨大潜力.
- 开发强大而公正的AI模型在很大程度上依赖于培训,验证和测试数据集的质量和多样性.
- 公共可用的医学成像数据集往往在质量和包容性方面存在局限性,特别是在传统上服务不足的全球人口方面.
研究的目的:
- 在公开可用的医学成像数据集中批判性地评估人口,地理,遗传和疾病的代表性.
- 识别和突出目前数据集中的缺陷,这些缺陷阻碍了公平有效的AI模型的开发.
- 倡导加强对综合数据集开发的重视,以推进AI在医学诊断中的影响和可靠性.
主要方法:
- 对现有的公共医学成像数据集进行全面的文献审查.
- 分析数据集以代表关键的人口,地理和遗传因素.
- 评估评估数据集中的疾病代表性的多样性.
主要成果:
- 公共可用的医学成像数据集经常缺乏对不同人口和地理区域的充分代表性.
- 在包括各种遗传因素和广泛的疾病方面观察到显著的差异.
- 目前公共数据集的现状表明,急需提高质量和包容性.
结论:
- 人工智能模型在疾病检测中的有效性和公平性直接受到用于其开发的数据集的质量和多样性的限制.
- 解决数据集表示中发现的差距至关重要,以确保人工智能工具在所有人群中都是有效和公平的.
- 为了充分实现人工智能在医学成像中的变革潜力,加强数据集开发的协调努力至关重要.
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