APPRAISE-AI 是人工智能研究量化评估的工具,用于临床决策支持
Jethro C C Kwong1,2, Adree Khondker1, Katherine Lajkosz1,3
1Division of Urology, Department of Surgery, University of Toronto, Toronto, Ontario, Canada.
JAMA network open
|September 25, 2023
概括
一个名为APPRAISE-AI的新工具量化了医疗保健中人工智能 (AI) 研究的质量. 这种可靠的方法有助于比较AI模型并改进临床决策支持工具.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 人工智能 (AI) 越来越多地被用于医疗保健,但人们对研究方法和公平性存在担忧.
- 目前的AI报告准则缺乏对整体研究质量的定量衡量,这阻碍了对类似临床问题的模型比较.
研究的目的:
- 开发和验证APPRAISE-AI工具,用于评估用于临床决策支持的AI预测模型的方法和报告质量.
主要方法:
- 在AI研究中,应用了APPRAISE-AI工具,这是一种量化方法,在24个项目中评估了6个领域 (临床相关性,数据质量,方法,稳定性,报告,可重复性).
- 该工具在对用于败血症预测的机器学习模型的系统审查中进行了测试.
- 评估了interrater和intrarater可靠性,以及与专家得分,引用率,QUADAS-2和TRIPOD遵守率的相关性.
主要成果:
- APPRAISE-AI表现出强大的interrater和intrarater可靠性 (ICC> 0.74). 这是一个很好的结果.
- 来自APPRAISE-AI的分数与专家评估 (ρ=0.82),引用率 (ρ=0.69),QUADAS-2域 (ρ=0.56) 和TRIPOD坚持 (ρ=0.87) 有显著的相关性.
- 包括的研究显示质量中等,数据来源,样本大小计算,偏差评估,错误分析和透明度存在共同的弱点.
结论:
- APPRAISE-AI是一个可靠的工具,用于定量评估AI研究在临床决策支持中的质量.
- 这个工具可以帮助研究人员,审稿人,编辑和资助者比较人工智能研究质量.
- 这些发现突出了人工智能研究方法和报告的改进领域.
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