SYSTEMATIC REVIEW article
Front. Public Health
Sec. Digital Public Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1609615
This article is part of the Research TopicAdvancing Public Health through Generative Artificial Intelligence: A Focus on Digital Well-Being and the Economy of AttentionView all 6 articles
Artificial Intelligence in Early Warning Systems for Infectious Disease Surveillance: A Systematic Review
Provisionally accepted
- University of Texas Southwestern Medical Center, Dallas, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: Infectious 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.Objective: This systematic review evaluates the current landscape of AI applications in EWS for infectious disease surveillance, identifying key techniques, data sources, benefits, and challenges.Methods: Following PRISMA guidelines, a systematic search of Semantic Scholar (2018-onward) yielded 67 relevant studies after screening 600 records and removing duplicates and non-relevant articles.
Keywords: artificial intelligence, Public Health, disease surveillance, Early warning system (EWS), Infectious Disease, Systematic review
Received: 10 Apr 2025; Accepted: 02 Jun 2025.
Copyright: © 2025 Villanueva-Miranda, Xiao and Xie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yang Xie, University of Texas Southwestern Medical Center, Dallas, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.