School of Health Policy and Management, York University, Toronto, ON, Canada.
Department of Psychology, York University, Toronto, ON, Canada.
Health Informatics J. 2024 Jul-Sep;30(3):14604582241275844. doi: 10.1177/14604582241275844.
Timely detection of disease outbreaks is critical in public health. Artificial Intelligence (AI) can identify patterns in data that signal the onset of epidemics and pandemics. This scoping review examines the effectiveness of AI in epidemic and pandemic early warning systems (EWS). To assess the capability of AI-based systems in predicting epidemics and pandemics and to identify challenges and strategies for improvement. A systematic scoping review was conducted. The review included studies from the last 5 years, focusing on AI and machine learning applications in EWS. After screening 1087 articles, 33 were selected for thematic analysis. The review found that AI-based EWS have been effectively implemented in various contexts, using a range of algorithms. Key challenges identified include data quality, model explainability, bias, data volume, velocity, variety, availability, and granularity. Strategies for mitigating AI bias and improving system adaptability were also discussed. AI has shown promise in enhancing the speed and accuracy of epidemic detection. However, challenges related to data quality, bias, and model transparency need to be addressed to improve the reliability and generalizability of AI-based EWS. Continuous monitoring and improvement, as well as incorporating social and environmental data, are essential for future development.
及时发现疾病暴发对于公共卫生至关重要。人工智能（AI）可以识别数据中的模式，这些模式表明疫情和大流行的发生。本范围审查研究了人工智能在疫情和大流行预警系统（EWS）中的有效性。目的是评估基于人工智能的系统在预测疫情和大流行方面的能力，并确定挑战和改进策略。进行了系统的范围审查。该审查包括过去 5 年的研究，重点是 EWS 中的人工智能和机器学习应用。在筛选了 1087 篇文章后，选择了 33 篇进行主题分析。审查发现，基于人工智能的 EWS 已在各种情况下有效实施，使用了多种算法。确定的主要挑战包括数据质量、模型可解释性、偏差、数据量、速度、多样性、可用性和粒度。还讨论了减轻人工智能偏差和提高系统适应性的策略。人工智能在提高疫情检测速度和准确性方面显示出了潜力。然而，需要解决与数据质量、偏差和模型透明度相关的挑战，以提高基于人工智能的 EWS 的可靠性和通用性。持续监测和改进以及纳入社会和环境数据对于未来的发展至关重要。
