在放射学中建立多样性,公平性和包容性 人工智能:代表性很重要,从数据到劳动力
Florence X Doo1, Geraldine B McGinty2
1Director of Innovation, University of Maryland Medical Intelligent Imaging Center (UM2ii), Baltimore, Maryland; Member, Committee on Economics in Academic Radiology, under the ACR Commission on Economics.
多样性,公平性和包容性 (DEI) 对于开发无偏的放射学人工智能 (AI) 至关重要. 在AI设计和领导中嵌入DEI原则可以确保公平的患者护理,并防止健康差异.
科学领域:
- 放射学 人工智能 放射学 人工智能
- 医疗保健 公平 医疗保健 公平
- 医疗信息学 医疗信息学
背景情况:
- 多样性,公平性和包容性 (DEI) 对于推动放射学人工智能 (AI) 的发展至关重要.
- 当前AI在医疗保健中的发展和部署有可能使现有的健康不平等现存.
- 人工智能领导和研究中缺乏多元化的代表性是一个重大问题.
研究的目的:
- 突出DEI在放射学AI的开发和部署中的关键作用.
- 强调需要在整个AI生命周期中整合DEI原则.
- 提出解决方案,促进一个多样化和包容性的AI驱动的医疗保健未来.
主要方法:
- 在AI设计和数据集中分析DEI原则.
- 检查DEI对AI模型准确性和患者护理的影响.
- 审查在放射学AI领导和研究中的代表性.
主要成果:
- 偏见的数据集可能导致不准确和歧视性的AI模型.
- 为了减轻健康不平等,需要多元化的领导和研究团队.
- 有针对性的DEI培训计划可以培养放射学AI的未来领导者.
结论:
- 将DEI整合到放射学AI中是一个道德和实际的必要条件.
- 需要采取积极的措施,以确保人工智能对所有患者群体都有平等的益处.
- 发展多元化的领导者管道是包容性AI支持的医疗保健系统的关键.
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