在随机对照试验中检测人工智能医疗器械的算法错误和患者伤害:系统审查协议
Aditya U Kale1,2,3,4, Henry David Jeffry Hogg5, Russell Pearson6
1Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom.
JMIR research protocols
|June 28, 2024
概括
本系统性审查评估了人工智能 (AI) 医疗器械错误和临床试验中的不良事件. 它强调需要对人工智能性能错误和患者损害进行严格分析,以确保所有人群的安全.
科学领域:
- 医疗技术 医疗技术 医学技术
- 临床研究 临床研究
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 医疗设备在临床工作流程,诊断,预后和治疗决策中提供了变革性的潜力.
- 确保AI医疗设备在不同人群中的安全性至关重要.
- 现有文献强调需要严格的性能错误分析,以防止算法偏差或特定失败模式等问题对患者造成伤害.
研究的目的:
- 在AI医疗器械的随机对照试验 (RCT) 中系统地审查AI错误和不良事件 (AE) 的频率和严重程度.
- 探索用于分析AI性能错误的方法,包括子组结果调查.
- 在临床研究中发现报告和分析AI相关危害的漏洞.
主要方法:
- 对评估人工智能医疗器械的RCT进行系统审查,通过MEDLINE,Embase,Cochrane CENTRAL和临床试验注册表进行搜索.
- 主要结局包括AI错误的频率和严重程度,患者的伤害和报告的AE.
- 使用Cochrane风险偏差工具 (RoB2) 进行质量评估,数据分析比较错误率和手臂之间的患者伤害,以及潜在的元分析.
主要成果:
- 预先的搜索和战略设计已经完成;标题/摘要选于2023年9月开始,全文选和数据收集正在进行中.
- 该审查已在PROSPERO (CRD42023387747) 上注册.
- 数据收集和分析于2024年4月开始.
结论:
- 人工智能医疗器械研究的可变报告需要标准化的方法来评估安全性.
- 严格检测,分析和报告性能错误和患者伤害对于强大的AI安全评估至关重要.
- 这一审查将提供关于人工智能错误和损害的频率和严重性的见解,为整体和子组绩效提供更好的分析方法.
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