放射学AI部署和评估表 (RADAR) 将基于价值的AI引入放射学实践
Bart-Jan Boverhof1, W Ken Redekop1, Daniel Bos2,3
1Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands.
Insights into imaging
|February 5, 2024
概括
雷达框架提供了一个结构化的方法来评估人工智能 (AI) 在放射学中的价值. 这种全面的七级系统确保了人工智能技术从开发到本地实施的有效评估.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 卫生技术评估 卫生技术评估
背景情况:
- 人工智能 (AI) 在放射学领域的快速发展需要强大的价值评估方法.
- 现有的框架可能无法充分解决AI技术在医疗保健中的独特生命周期和本地实施需求.
研究的目的:
- 引入RADAR框架,这是一个新的,层次化的系统,用于对放射学中的AI进行全面评估.
- 调整和扩展现有的有效性框架,以涵盖人工智能价值的全部范围,从技术性能到当地影响.
主要方法:
- 雷达框架是从已建立的成像效率模型中改编出来的,它包含了一个新的"局部效率"级别.
- 它采用七个层次的层次结构 (RADAR-1到RADAR-7) 来指导AI部署的不同阶段的估值.
- 使用各种研究设计,包括in silico试验,横截面研究,RCT,队列研究,健康经济评估和预算影响分析.
主要成果:
- 雷达提供了一个结构化,七层次的层次结构,用于评估放射学AI在整个生命周期中的价值.
- 它区分了评估阶段,从临床前 (技术,诊断疗效) 到实施后 (结果,成本效益,本地翻译).
- 该框架适应了各种各样的估值需求,从初始技术验证到社会成本效益和当地整合.
结论:
- 雷达框架提供了一个全面和动态的方法来评估AI在放射学.
- 它的层次结构和对当地相关性的关注与基于价值的放射学原则保持一致.
- 雷达解决了AI系统评估中的一个关键缺口,促进了其有效的临床整合.
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