Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

人工智能（AI）是一种有价值的工具，可以广泛用于为临床和公共卫生决策提供信息，以有效管理大流行的影响。本次范围审查的目的是从同行评审、预印本和灰色文献中确定人工智能在大流行病防范和应对中的关键用例。数据综合分为两部分：对利用机器学习 (ML) 技术的研究进行深入回顾，以及对应用传统建模方法的研究进行有限回顾。深入审查中的机器学习应用程序被分类为与公共卫生和临床实践相关的用例，并进行了叙述性综合。一百八十三篇文章符合深入审查的纳入标准。确定了六个关键用例：预测传染病动态和干预措施的效果；监测和疫情爆发检测；实时监控公共卫生建议的遵守情况；实时检测流感样疾病；对感染进行分类和及时诊断；以及疾病的预后和对治疗的反应。有用的数据源和 ML 类型因用例而异。搜索发现了 1167 篇关于传统建模方法的文章，这些文章强调了可以利用机器学习来提高估计或预测准确性的其他领域。重要的基于机器学习的解决方案是为了应对流行病而开发的，特别是针对 COVID-19，但很少有针对流行病早期的实际应用进行优化的。这些发现可以支持政策制定者、临床医生和其他利益相关者优先考虑研究和开发，以支持人工智能在未来流行病中的应用。