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在流行病学与公共卫生和医学中引导人工智能:一个生命周期框架,以减轻人工智能错位

Ahmed Hassoon¹, Christine Lin², Hyun Yi Jacqualine Woo³

¹Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Neurology, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins Whiting School of Public Health, Baltimore, MD, USA.

Annals of epidemiology

|November 22, 2025

概括

公共卫生中的人工智能 (AI) 面临着对齐失败,而不仅仅是数据偏差. 这项研究提出了一个七阶段的流行病学框架,以确保人工智能系统是值得信赖,安全和公平的.

科学领域:

公共卫生公共卫生
人工智能的人工智能
流行病学流行病学

背景情况:

人工智能 (AI) 为公共卫生领域的进步提供了巨大的潜力.
然而,人工智能系统可能无法与人类价值观保持一致,这可能会加剧健康差距.
算法偏差通常被狭地视为数据问题,忽视了更广泛的生命周期问题.

研究的目的:

挑战算法偏见的有限视角,将其仅仅视为数据问题.
在整个开发生命周期中引入一个全面的七阶段框架,用于识别和减轻AI调整失败.
促进用于公共卫生应用的可信,安全和公平的人工智能系统的创建.

主要方法:

一个七阶段的框架,整合了整个AI开发生命周期的流行病学原则:问题定义,团队组建,研究设计,数据采集,模型培训,验证和部署后实施.
系统地整合核心流行病学概念:人口代表性,严格的研究设计,偏见表征和因果推理.
在每个阶段确定特定的调整失败,并提出基于证据的解决方案.

主要成果:

人工智能系统的错位可能发生在开发生命周期的任何阶段,而不仅仅是在数据采集期间.
拟议的流行病学框架提供了一个结构化的方法,以主动解决调整风险.
针对每个阶段提供可操作的解决方案,以减轻故障,从初始问题制定到市场后的性能.

结论:

在整个AI生命周期中嵌入流行病学严谨性对于在公共卫生中开发可靠和公平的AI至关重要.
这种系统性方法对于利用人工智能的好处至关重要,同时防止健康不平等的恶化.
该框架指导研究人员,开发人员和决策者创造出既具有变革性又在道德上健全的AI.

关键词:

人工智能对齐对齐算法偏差是一种算法偏差.调整问题调整问题人工智能的人工智能是人工智能.流行病学流行病学卫生公平性健康公平性医疗保健技术医疗保健技术医疗保健技术生命周期管理生命周期管理机器学习机器学习公共卫生信息学公共卫生信息学

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