医学中的人工智能:通过质量保证,质量控制和验收测试来减轻风险并最大限度地提高收益
Usman Mahmood1, Amita Shukla-Dave1,2, Heang-Ping Chan3
1Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, United States.
BJR artificial intelligence
|March 13, 2024
概括
在医学领域实施人工智能 (AI) 需要具体的质量保证 (QA) 和质量控制 (QC) 措施. 强大的协议,包括验收测试 (AT),持续监测和用户培训,对于安全的AI集成至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床质量管理临床质量管理
背景情况:
- 人工智能 (AI) 工具在医疗环境中越来越多地被采用,这给已建立的临床工作流程带来了重大挑战.
- 确保AI在医疗保健中的安全性,有效性和公平性,需要采取积极的质量保证和控制方法.
研究的目的:
- 讨论对医学人工智能工具的特定环境质量保证 (QA) 措施的关键需求.
- 为临床AI实施概述验收测试 (AT) 和质量保证协议的基本组成部分.
- 强调利益相关者对人工智能在医疗保健中安全整合的共同责任.
主要方法:
- 评论回顾了当前的挑战,并提出了AI医疗应用中QA/QC的框架.
- 它详细介绍了验收测试 (AT) 和持续质量控制 (QC) 监测的基本要素.
- 使用现实世界的例子来说明拟议的质量保证/质量控制策略的实际应用.
主要成果:
- 医疗AI的有效QA/QC需要多方面的方法,包括临床前验收测试 (AT),持续的质量控制 (QC) 监测和全面的用户培训.
- 确定并讨论了人工智能工具的AT和QA的关键组件.
- 评论强调了制造商,监管机构,医疗保健系统,医学物理学家和临床医生之间合作的必要性.
结论:
- 强大的,特定于环境的质量保证 (QA) 和质量控制 (QC) 对于AI在医学中的安全和公平采用是不可或缺的.
- 结构化的方法包括验收测试 (AT),持续监测和用户教育至关重要.
- 所有利益相关者共同承担责任对于成功将AI整合到临床实践中至关重要.
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