PERSPECTIVE article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1645467
AI-Driven Epidemic Intelligence: The Future of Outbreak Detection and Response
Provisionally accepted
- 1National Research Council Canada (NRC), Ottawa, Canada
- 2Faculty of Health, University of Waterloo, Waterloo, Canada
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Epidemic intelligence, the process of detecting, verifying, and analyzing public health threats to enable timely responses, traditionally relies heavily on manual reporting and structured data, often causing delays and coverage gaps. The growing frequency of emerging infectious diseases highlights the urgency for more rapid and accurate surveillance methods. This perspective proposes a forward-looking conceptual framework for AI-driven epidemic intelligence, emphasizing the transformative potential of integrating large language models (LLMs), natural language processing (NLP), and optimization-based resource allocation strategies. While existing AI-driven systems have shown significant capabilities during the COVID-19 pandemic, several challenges remain, including real-time adaptability, multilingual data handling, misinformation, and public health policy alignment. To address these gaps, we propose an integrated, real-time adaptable LLM-based epidemic intelligence system, capable of correlating cross-source data, optimizing healthcare resource allocation, and supporting informed outbreak response. This approach aims to significantly improve early warning capabilities, enhancing forecasting accuracy, and strengthen pandemic preparedness.
Keywords: epidemic intelligence, artificial intelligence, Outbreak detection, Large language models, Pandemic preparedness, Real-time surveillance
Received: 11 Jun 2025; Accepted: 17 Jul 2025.
Copyright: © 2025 Kaur and Butt. 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: Zahid Ahmad Butt, Faculty of Health, University of Waterloo, Waterloo, Canada
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