Bazzano Alessandra N, Mantsios Andrea, Mattei Nicholas, Kosorok Michael R, Culotta Aron
Department of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Center for Community-Engaged Artificial Intelligence, School of Science & Engineering, Tulane University, New Orleans, LA, United States.
J Med Internet Res. 2025 Jan 22;27:e68198. doi: 10.2196/68198.
There is a critical need for community engagement in the process of adopting artificial intelligence (AI) technologies in public health. Public health practitioners and researchers have historically innovated in areas like vaccination and sanitation but have been slower in adopting emerging technologies such as generative AI. However, with increasingly complex funding, programming, and research requirements, the field now faces a pivotal moment to enhance its agility and responsiveness to evolving health challenges. Participatory methods and community engagement are key components of many current public health programs and research. The field of public health is well positioned to ensure community engagement is part of AI technologies applied to population health issues. Without such engagement, the adoption of these technologies in public health may exclude significant portions of the population, particularly those with the fewest resources, with the potential to exacerbate health inequities. Risks to privacy and perpetuation of bias are more likely to be avoided if AI technologies in public health are designed with knowledge of community engagement, existing health disparities, and strategies for improving equity. This viewpoint proposes a multifaceted approach to ensure safer and more effective integration of AI in public health with the following call to action: (1) include the basics of AI technology in public health training and professional development; (2) use a community engagement approach to co-design AI technologies in public health; and (3) introduce governance and best practice mechanisms that can guide the use of AI in public health to prevent or mitigate potential harms. These actions will support the application of AI to varied public health domains through a framework for more transparent, responsive, and equitable use of this evolving technology, augmenting the work of public health practitioners and researchers to improve health outcomes while minimizing risks and unintended consequences.
在公共卫生领域采用人工智能（AI）技术的过程中，社区参与至关重要。公共卫生从业者和研究人员在疫苗接种和环境卫生等领域一直有所创新，但在采用生成式AI等新兴技术方面则较为迟缓。然而，随着资金、项目规划和研究要求日益复杂，该领域如今面临一个关键时刻，需要提高其灵活性和对不断演变的健康挑战的应对能力。参与式方法和社区参与是许多当前公共卫生项目和研究的关键组成部分。公共卫生领域有能力确保社区参与成为应用于人群健康问题的人工智能技术的一部分。没有这种参与，这些技术在公共卫生领域的采用可能会将很大一部分人口排除在外，尤其是那些资源最少的人群，从而有可能加剧健康不平等。如果在设计公共卫生领域的人工智能技术时考虑到社区参与、现有的健康差距以及改善公平性的策略，那么隐私风险和偏见的持续存在更有可能得到避免。这一观点提出了一种多方面的方法，以确保人工智能在公共卫生领域更安全、更有效地整合，并提出以下行动呼吁：（1）将人工智能技术的基础知识纳入公共卫生培训和专业发展；（2）采用社区参与方法共同设计公共卫生领域的人工智能技术；（3）引入治理和最佳实践机制，以指导公共卫生领域人工智能的使用，预防或减轻潜在危害。这些行动将通过一个框架支持人工智能在不同公共卫生领域的应用，以更透明、更具响应性和更公平地使用这一不断发展的技术，增强公共卫生从业者和研究人员的工作，以改善健康结果，同时将风险和意外后果降至最低。
