行动中的人工智能:放射学人工智能理事会的路线图,用于有效的模型评估和部署
Hari Trivedi1, Bardia Khosravi2, Judy Gichoya3
1Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia; Chair, Radiology AI Council; Codirector, HITI Lab; Director, AI Image Extraction Core, Emory University.
Journal of the American College of Radiology : JACR
|May 25, 2025
概括
放射学AI委员会制定了一个标题,以标准化临床工作流程中人工智能 (AI) 模型的评估和部署. 该框架确保了透明和整体的AI模型评估,以提高放射学中的有效性和安全性.
科学领域:
- 放射学 放射学是一门学科.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 越来越多地融入放射学,需要标准化的评估和部署流程.
- 人工智能部署的挑战包括现实世界的性能,工作流集成和资源配置.
研究的目的:
- 概述创建一个放射学AI委员会和一个框架 (标题) 来评估和安装AI模型.
- 确保在放射学中选择AI模型的标准化和透明流程.
主要方法:
- 在学术中心建立放射学AI委员会.
- 开发一个全面的标题来评估AI模型.
- 在8个月的时间内,使用标题对13个AI模型进行了评估.
主要成果:
- 该标题解决了部署的关键挑战:现实世界的绩效,工作流程,资源,投资回报率和卫生系统的影响.
- 最初对13个模型的评估为该标题的应用提供了洞察力.
- 该过程强调了超越绩效指标的整体评估.
结论:
- 一个标准化,透明的标签对于成功地将AI整合到放射学中至关重要.
- 整体评估和客观评估对于提高人工智能的有效性和安全性至关重要.
- 开发的框架支持将人工智能模型有效纳入临床工作流程.
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