概括
在医疗保健中,对人工智能 (AI) 的有效部署后监测至关重要. 一个新的框架通过跟踪完整性,性能和影响来确保人工智能系统保持安全,有效和有益.
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
- 临床决策支持 临床决策支持
背景情况:
- 医疗保健人工智能 (AI) 的部署后监测对于安全,质量和治理至关重要.
- 确保人工智能系统继续提供好处,并按预期运行,需要强有力的监督.
研究的目的:
- 开发和介绍一个实用的框架,用于监控在医疗保健部署的AI系统.
- 引导卫生系统制定可行的AI监测计划.
主要方法:
- 开发了一个基于三个原则的框架:系统完整性,性能和影响.
- 以一个学术医疗中心的例子为例,用于传统和生成AI.
- 提供有关指标,审查时间表,责任和后续行动的指导.
主要成果:
- 该框架在斯坦福医疗保健积极使用.
- 它涉及系统正常运行时间,错误检测,IT生态系统变化,数据漂移以及临床/患者价值.
- 为制定全面的监测计划提供了实际指导.
结论:
- 该框架为卫生系统提供了一个模板,以确保人工智能部署是安全和有效的.
- 实施挑战包括资源限制和组织复杂性.
- 持续监测对于在医疗保健中持续获得人工智能的好处至关重要.
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