医疗人工智能报告准则:系统审查和元分析
Fiona R Kolbinger1,2,3,4,5,6, Gregory P Veldhuizen1, Jiefu Zhu1
1Else Kroener Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
Communications medicine
|April 11, 2024
概括
关于人工智能 (AI) 在医学中的报告准则有很大的不同. 建立共同的标准对于提高人工智能医疗研究的可靠性,可复制性和可信度至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 人工智能 (AI) 在医学上提供了巨大的潜力.
- 缺乏通用报告准则阻碍了人工智能研究的有效性和可重现性.
研究的目的:
- 分析医疗研究中现有的AI报告准则.
- 为了识别当前标准中的变化和差距.
主要方法:
- 对26条报告准则 (2009-2023) 的系统审查.
- 分析按指导范围,共识质量和研究阶段 (临床前,转化,临床) 分层.
主要成果:
- 人工智能报告准则在共识质量,广度和研究阶段适用性方面存在差异.
- 共同项目包括研究设计和模型性能报告.
- 其他项目是特定于领域或特定于阶段的.
结论:
- 需要共同的标准来解决AI报告准则中的差异和差距.
- 改进的指导方针将提高AI医疗保健的可靠性,可重复性和公众对AI医疗保健的信任.
- 这有助于将人工智能安全,有效和道德地转化为临床实践.
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