在医疗保健系统中大规模部署人工智能的实施框架
Hassan Sami Adnan1, Amitis Shidani2, Lei Clifton1,3
1Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
iScience
|May 19, 2025
概括
一个新的框架帮助利益相关者共同创建和实施人工智能 (AI) 和机器学习 (ML) 在医疗保健中. 这种以人为中心的方法解决了部署人工智能的挑战,以获得更好的临床结果和个性化医疗.
科学领域:
- 数字健康数字健康
- 人工智能在医学中的应用
- 机器学习应用 机器学习应用
背景情况:
- 人工智能 (AI) 和数字健康技术越来越多地融入医学.
- 显著的障碍阻碍了人工智能在医疗保健系统中的大规模部署.
- 现有的方法往往不足以解决人工智能模型实施的完整生命周期.
研究的目的:
- 引入新的实施框架,共同创建和部署医疗保健中的AI和机器学习 (ML) 模型.
- 通过整个AI模型生命周期指导各种利益相关者,从设计到维护.
- 促进以人为中心的人工智能设计,以提高临床效用和个性化医疗.
主要方法:
- 采用设计思维方法,以促进设计师,开发人员,患者和决策者之间的合作.
- 该框架将隐私保护与临床参数相结合,以创建强化学习的奖励函数.
- 可解释的人工智能 (xAI) 方法用于临床解释性,由治理和编排平台支持.
主要成果:
- 该框架允许在整个AI模型生命周期中共同创建和解决问题.
- 它通过根据定义的奖励函数对竞争模型进行排名,促进了超出简单预测的临床效用.
- 治理和编排平台的整合允许有效的模型监控和管理.
结论:
- 拟议的框架有助于在复杂的卫生系统中系统地实施AI和ML.
- 它支持AI增强健康解决方案的开发,重点是临床实用性和可解释性.
- 这种以人为中心的方法对于克服障碍和实现AI在医疗保健中的潜力至关重要.
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