基于人工智能的健康技术的绩效评估,可解释性和可解释性:医疗保健利益相关者需要知道的内容
Line Farah1,2, Juliette M Murris3,4,5, Isabelle Borget1,6,7
1Groupe de Recherche et d'accueil en Droit et Economie de la Santé Department, University Paris-Saclay, Orsay, France.
Mayo Clinic proceedings. Digital health
|April 10, 2025
概括
本次审查强调了性能,可解释性和可解释性对于开发可靠的人工智能 (AI) 医疗器械 (MD) 至关重要. 它为评估这些AI-MD标准的监管合规性和利益相关者的问责制提供了指导.
科学领域:
- 医疗器械开发 医疗器械开发
- 医疗保健中的人工智能
- 卫生技术评估 卫生技术评估
背景情况:
- 基于AI的医疗器械 (MDs) 的开发需要强大的评估框架.
- 医疗技术评估 (HTA) 的现有指导方针需要针对AI-MDs制定具体的标准.
- 对基于AI的MD的信心取决于理解算法行为.
研究的目的:
- 确定基于AI的MD开发的基本概念.
- 分析AI-MDs的评估方法,重点关注性能,可解释性和可解释性.
- 为AI-MD开发和监管评估提供建议和决策支持.
主要方法:
- 文献审查以确定AI-MDs的关键HTA标准.
- 对所选标准的现有评估方法的分析.
- 对HTA机构指导方针的范围审查.
主要成果:
- 性能,可解释性和可解释性是AI-MD信心的关键标准.
- 提供了基于模型结构和数据的AI-MD绩效评估的建议.
- 描述的方法以支持对可解释性和可解释性的评估.
- 建议制定监管要求的决策支持流程图.
结论:
- 在HTA中,越来越强调AI-MD问责制的可解释性和可解释性.
- 建议的工具和方法有助于理解AI算法预测和决策过程.
- 清晰的标准和评估方法对于基于AI的MD的安全和有效开发至关重要.
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