人工智能对影响的AI 评估人工智能支持的临床工具的长期现实影响的框架:系统审查和叙述综合
Christine Jacob1, Noé Brasier2, Emanuele Laurenzi1
1FHNW, University of Applied Sciences and Arts Northwestern Switzerland, Windisch, Switzerland.
Journal of medical Internet research
|February 5, 2025
概括
一个新的框架,AI for IMPACTS,评估了超越技术指标的医疗保健中的人工智能. 它评估了现实世界的整合,性能和可扩展性,以获得更好的临床采用和长期可行性.
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
- 医疗保健系统分析 医疗保健系统分析
背景情况:
- 人工智能 (AI) 具有改变医疗保健提供和效率的巨大潜力.
- 人工智能的临床采用受到不足的评估框架的阻碍,这些评估框架仅集中在技术指标上.
- 现有的方法忽略了关键的现实世界因素,如临床影响,整合和经济可行性.
研究的目的:
- 开发一个全面的框架来评估医疗保健中的AI.
- 该框架超出了技术性能的范围,包括社会和组织方面的维度.
- 专注于成功的,长期的AI在临床环境中的实际实施所必需的标准.
主要方法:
- 系统性文献审查于2024年7月在主要科学数据库 (PubMed,Cochrane,Scopus,IEEE Xplore) 进行.
- 包括2019年1月至2024年7月期间发表的44项研究,遵守PRISMA和Cochrane指南.
- 使用NVivo进行的主题分析和叙事合成,确定了关键的评估主题.
主要成果:
- 开发了AI for IMPACTS框架,整合了临床背景和现实世界的实施因素.
- 该框架包括七个集群:集成,监测,性能,可接受性,成本,技术安全和可扩展性 (IMPACTS).
- 每个集群都包含特定的子标准,为AI工具评估提供了整体方法.
结论:
- "AI for IMPACTS"框架提供了一种整体方法,用于评估AI在医疗保健中的长期实际影响.
- 建议通过专家共识和现实世界的测试进行进一步的验证.
- 强调需要多学科专业知识,评估员培训和适应性评估流程来整合人工智能.
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