McKee Martin, Rosenbacke Rikard, Stuckler David
European Observatory on Health Systems and Policies, London School of Hygiene and Tropical Medicine, London, UK.
Department of Accounting, Centre for Corporate Governance, Copenhagen Business School, Frederiksberg, Denmark.
Int J Health Plann Manage. 2025 Jan;40(1):257-270. doi: 10.1002/hpm.3864. Epub 2024 Oct 27.
Artificial intelligence (AI) applications are complex and rapidly evolving, and thus often poorly understood, but have potentially profound implications for public health. We offer a primer for public health professionals that explains some of the key concepts involved and examines how these applications might be used in the response to a future pandemic. They include early outbreak detection, predictive modelling, healthcare management, risk communication, and health surveillance. Artificial intelligence applications, especially predictive algorithms, have the ability to anticipate outbreaks by integrating diverse datasets such as social media, meteorological data, and mobile phone movement data. Artificial intelligence-powered tools can also optimise healthcare delivery by managing the allocation of resources and reducing healthcare workers' exposure to risks. In resource distribution, they can anticipate demand and optimise logistics, while AI-driven robots can minimise physical contact in healthcare settings. Artificial intelligence also shows promise in supporting public health decision-making by simulating the social and economic impacts of different policy interventions. These simulations help policymakers evaluate complex scenarios such as lockdowns and resource allocation. Additionally, it can enhance public health messaging, with AI-generated health communications shown to be more effective than human-generated messages in some cases. However, there are risks, such as privacy concerns, biases in models, and the potential for 'false confirmations', where AI reinforces incorrect decisions. Despite these challenges, we argue that AI will become increasingly important in public health crises, but only if integrated thoughtfully into existing systems and processes.
人工智能（AI）应用复杂且发展迅速，因此常常难以被充分理解，但对公共卫生有着潜在的深远影响。我们为公共卫生专业人员提供一份入门指南，解释其中涉及的一些关键概念，并探讨这些应用在应对未来大流行时可能如何被使用。它们包括早期疫情检测、预测建模、医疗管理、风险沟通和健康监测。人工智能应用，尤其是预测算法，有能力通过整合社交媒体、气象数据和手机移动数据等各种数据集来预测疫情爆发。人工智能驱动的工具还可以通过管理资源分配和减少医护人员面临的风险来优化医疗服务。在资源分配方面，它们可以预测需求并优化物流，而人工智能驱动的机器人可以减少医疗环境中的身体接触。人工智能在通过模拟不同政策干预的社会和经济影响来支持公共卫生决策方面也显示出前景。这些模拟有助于政策制定者评估封锁和资源分配等复杂情况。此外，它可以增强公共卫生信息传递，在某些情况下，人工智能生成的健康信息比人工生成的信息更有效。然而，也存在风险，如隐私问题、模型偏差以及“错误确认”的可能性，即人工智能强化错误决策。尽管存在这些挑战，但我们认为，只有在深思熟虑地融入现有系统和流程的情况下，人工智能在公共卫生危机中将变得越来越重要。
