针对医疗应用的人工智能系统的质量管理
Lorenzo Mercolli1, Axel Rominger1, Kuangyu Shi1
1Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, CH-3010 Bern, Switzerland.
Zeitschrift fur medizinische Physik
|February 27, 2024
概括
在诊所实施人工智能 (AI) 需要强有力的质量管理. 本文提出了一个基于风险的框架,以确保可靠的AI模型预测和临床环境中的性能评估.
科学领域:
- 医疗信息学 医疗信息学
- 临床工程 临床工程
- 医疗保健中的人工智能
背景情况:
- 人工智能 (AI) 系统的临床实施面临障碍,原因是医疗器械认证要求和现有质量管理系统的整合挑战.
- 关键的用户关注点包括确保人工智能模型预测的稳定性,并建立模型性能的定期评估方法.
- 目前的实践缺乏在临床工作流程中管理AI的标准化方法.
研究的目的:
- 为机器学习 (ML) 系统的临床实施提供一个概念框架.
- 提出一个风险驱动的质量管理计划,适应基于AI的临床过程.
- 定义供应商和用户对人工智能系统监督的共同责任.
主要方法:
- 讨论临床ML实施的概念基础.
- 关于AAPM任务组100号报告第100号的建议 283 方法作为风险驱动的质量管理框架.
- 使用临床工作流程示例的插图来展示框架应用.
主要成果:
- 拟议的框架适用于AI系统,无论其固有的稳定性或用户专业知识如何.
- 框架内的风险评估可以系统地纳入ML系统的可解释性程度.
- 介绍了AI在临床环境中的质量管理的结构化方法.
结论:
- 基于既定方法的风险驱动的质量管理计划可以有效地将AI整合到临床程序中.
- 分享责任和系统的风险评估,包括可解释性,对于安全有效的AI部署至关重要.
- 该框架支持在医疗保健环境中强大的AI模型预测和质量评估.
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