人工智能模型文档在翻译科学中的作用:范围审查
Tracey A Brereton1, Momin M Malik1, Mark Lifson1
1Center for Digital Health, Mayo Clinic, Rochester, MN, United States.
Interactive journal of medical research
|July 14, 2023
概括
透明的文档对于人工智能 (AI) 和机器学习 (ML) 医疗建模软件 (MMS) 的成功临床翻译至关重要. 解决与偏见,问责制和可解释性相关的文档缺口将加速在医疗保健中采用人工智能.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 医疗保健服务研究 医疗服务研究
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 对医疗保健具有重大前景,但在临床采用方面面临挑战.
- 医疗建模软件 (MMS) 的透明文档缺口阻碍了AI/ML工具从研究转化为实践.
- 缺乏标准化的报告阻碍了实施基于AI/ML的临床决策支持的证据生成.
研究的目的:
- 为基于AI和ML的MMS提供现有文档实践的范围.
- 确定文档在将道德和可解释的MMS转化为临床工作流程中的作用.
- 阐明AI/ML MMS翻译的障碍和促进因素.
主要方法:
- 根据PRISMA-ScR指南进行了范围审查.
- 在PubMed的搜索和参考列表中发现了关于AI/ML MMS文档,伦理和可解释性的出版物.
- 一个两阶段的选过程和数据提取集中在文档障碍,标准和建议上.
主要成果:
- 115篇获取的论文中有21篇符合纳入标准,重点是MMS文档中的伦理和可解释性.
- 确定的主要挑战包括偏见,问责制,治理和可解释性.
- 建议强调主动评估,多学科合作,透明度,可追溯性和指导标准.
结论:
- 解决与文档相关的障碍 (偏见,问责,治理,可解释性) 对MMS临床翻译至关重要.
- 透明,战略性文件与翻译和监管科学保持一致,可以减少障碍.
- 改进的文档实践将促进在临床环境中采用基于AI/ML的MMS.
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