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临床人工智能的监测性能:一个范围审查协议

Eline Sandvig Andersen^1,2, Johan Baden Birk-Korch^1,2, Richard Röttger³

¹Department of Biochemistry and Immunology, Lillebaelt Hospital, Vejle, Denmark.

JBI evidence synthesis

|February 8, 2024

概括

本范围审查审查审查了监测临床人工智能 (AI) 系统的研究. 它确定了人工智能监测方法和影响其选择的因素,以改善患者护理.

科学领域:

医疗保健信息学医疗保健信息学
人工智能在医学中的应用
临床决策支持系统临床决策支持系统

背景情况:

临床人工智能 (AI) 的采用正在增加,但对其监测和质量保证的研究落后于开发和验证.
对实际实施的有限科学兴趣,特别是监测系统,需要进行全面的审查.
现有方法的异质性凸显了对临床AI监测的结构化概述的需要.

研究的目的:

系统地绘制临床人工智能 (AI) 系统监测研究的范围和性质.
确定用于监测临床AI性能的各种方法.
分析影响医疗保健中AI监控方法的选择和实施的因素.

主要方法:

采用了一种全面的范围审查方法.
在主要数据库 (MEDLINE,Embase,Scopus) 进行了搜索,并通过引用和灰色文献搜索进行了补充.
使用了系统选,独立审查员的数据提取,以及发现的叙述/图形呈现.

主要成果:

该审查综合了临床AI监测策略的当前研究.
确定用于在现实世界临床环境中评估AI性能的关键方法.
突出了影响这些监测方法的选择和应用的因素.

结论:

了解临床AI监测的情况对于安全有效的实施至关重要.
这一审查为开发标准化监测框架提供了基础.
需要进一步的研究来解决差距,并优化临床AI系统的质量保证.

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