确定开发的人工智能算法的关键特征,以实现对加拿大医疗保健的有意义影响:一个范围审查协议
Daouda Coulibaly1,2, Azadeh Bayani1,2, Bry Sylla3
1Laboratoire Transformation Numérique en Santé, Montréal, Québec, Canada.
BMJ open
|February 18, 2025
概括
这项研究审查了加拿大研究人员的人工智能 (AI) 算法,以改善患者护理. 结果将确定增强医疗保健影响的关键AI特征,并为未来加拿大的AI发展提供信息.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 加拿大卫生研究加拿大卫生研究.
背景情况:
- 有限的经验数据存在于医疗保健中人工智能 (AI) 采用的障碍,特别是在加拿大.
- 加拿大研究人员正在开发人工智能算法,用于患者分拣,诊断和护理管理.
- 了解AI算法特征对于最大限度地提高其在医疗保健中的影响至关重要.
研究的目的:
- 进行由加拿大附属研究人员开发的AI算法的范围审查.
- 识别和评估用于患者分拣,诊断和护理管理的AI算法.
- 确定人工智能算法的特征,可以增强它们在医疗保健中的影响.
主要方法:
- 根据JBI方法和PRISMA-ScR指南进行范围审查.
- 在MEDLINE,CINAHL和Web of Science数据库中搜索人工智能,临床管理和加拿大背景关键词.
- 包括2014年以后的英语/法语研究,详细介绍了加拿大附属研究人员为患者护理的AI算法.
主要成果:
- 从选定的文章中提取的数据的描述性分析.
- 结果将以表格格式呈现,并附有叙事摘要.
- 识别人工智能算法的关键特征,以改善医疗保健结果.
结论:
- 这项研究将提供有关加拿大医疗保健中人工智能的现状的见解.
- 结果将为克服障碍并增强AI影响的战略提供信息.
- 计划通过同行评审的出版物和会议演讲进行传播.
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