AUTHOR=Villanueva-Miranda Ismael , Xiao Guanghua , Xie Yang 

TITLE=Artificial intelligence in early warning systems for infectious disease surveillance: a systematic review

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1609615

DOI=10.3389/fpubh.2025.1609615

ISSN=2296-2565

ABSTRACT=IntroductionInfectious diseases pose a significant global health threat, exacerbated by factors like globalization and climate change. Artificial intelligence (AI) offers promising tools to enhance crucial early warning systems (EWS) for disease surveillance. This systematic review evaluates the current landscape of AI applications in EWS, identifying key techniques, data sources, benefits, and challenges.MethodsFollowing PRISMA guidelines, a systematic search of Semantic Scholar (2018-onward) was conducted. After screening 600 records and removing duplicates and non-relevant articles, the search yielded 67 relevant studies for review.ResultsKey findings reveal the prevalent use of machine learning (ML), deep learning (DL), and natural language processing (NLP), which often integrate diverse data sources (e.g., epidemiological, web, climate, wastewater). The major benefits identified include earlier outbreak detection and improved prediction accuracy. However, significant challenges persist regarding data quality and bias, model transparency (the “black box” issue), system integration difficulties, and ethical considerations such as privacy and equity.DiscussionAI demonstrates considerable potential to strengthen infectious disease EWS. Realizing this potential, however, requires concerted efforts to address data limitations, enhance model explainability, ensure ethical implementation, improve infrastructure, and foster collaboration between AI developers and public health experts.