从国家医学成像倡议的国际比较者学习人工智能发展:多阶段定性研究
Kassandra Karpathakis1, Emma Pencheon2, Dominic Cushnan3
1Decimal.health, Boston, MA, United States.
JMIR AI
|June 14, 2024
概括
这项研究分析了国际医学成像平台,以告知英格兰的国家AI成像平台. 主要建议侧重于可持续融资,算法验证和临床医生参与的工具.
科学领域:
- 医疗成像医学成像
- 人工智能 (AI) 是一种人工智能.
- 医疗信息学 医疗信息学
背景情况:
- 随着COVID-19大流行,人工智能驱动的医疗成像技术的投资在患者管理方面得到了加快.
- 英国国家COVID-19胸部成像数据库的成功促使创建了一个国家AI成像平台.
- 国家数字政策机构 (NHSX) 旨在将国家医疗成像平台推广为AI算法开发,验证和部署.
研究的目的:
- 检查用于AI算法开发和实施的医学成像平台的国际使用案例.
- 为英格兰国家医学成像平台的战略设计和开发提供信息.
主要方法:
- 国家卫生服务 (NHS) 人工智能实验室政策和战略团队采用了多阶段方法.
- 方法包括确定和优先考虑国家人工智能成像平台,进行政治,经济,社会,技术,法律和环境 (PESTLE) 分析,并与利益相关者进行半结构面试.
- 一个研讨会综合了见解和明智的政策建议.
主要成果:
- 使用PESTLE因子分析了七个国际AI成像平台.
- 采访了13名国际利益相关者,产生了8个关键主题.
- 制定了12项政策建议,以指导英国国家医学成像平台的发展.
结论:
- 人工智能算法开发通常发生在孤岛,需要国际合作.
- 从国际同行学习的经验凸显了需要可证明的平台好处,强大的验证,以及非技术用户的工具.
- 政策制定者必须利用国家和国际的经验来成功实施技术医疗保健计划.
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