ArticleFrontiers in artificial intelligence2025
AI-driven epidemic intelligence: the future of outbreak detection and response.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A machine learning approach for differential classification of chikungunya virus: an explainable AI study based on surveillance data.Infection · 2026Article
- Emerging Viral Zoonoses: Epidemiology, Vaccination Strategies, and Implications for Global Public Health.Vaccines · 2026Review
- Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era.New microbes and new infections · 2026Article
- Public Health Responsible AI Capability (PH-RAIC) Framework: A Conceptual Model for Integrating AI into Public Health Agencies.Healthcare (Basel, Switzerland) · 2026Article
- Artificial Intelligence For 6P Medicine: Consolidating AI Needs of Predictive, Preventive, Personalized, Participatory, Precision, and Public Health Trajectories.Journal of medical systems · 2026Review
- Role of Digital Health Technologies and Artificial Intelligence in Modern Public Health Surveillance.Cureus · 2026Review
- Containment of antimicrobial resistance for strengthening global public health security: Biorisk management perspectives.Biosafety and health · 2026Review
- Converging infectious disease threats in the post-COVID era: surveillance fragility, pandemic risk, and global preparedness.Frontiers in public health · 2026Review
- From fragmented to integrated surveillance in LMICs: digital pathways for outbreak detection and vaccine intelligence.Frontiers in public health · 2026Review
- Artificial intelligence for coordinating vaccine design, antiviral discovery, and real-world monitoring in the era of emerging and endemic viral threats.Frontiers in pharmacology · 2026Review
- Advancing epidemic intelligence: evaluating Senegal's mpox surveillance system and readiness for AI-driven predictive modelling.Frontiers in public health · 2026Article
- Structural Barriers and the Future of AI in Global Health: Lessons From Eradicable Diseases.Cureus · 2025Review
- AI-powered analysis of viral metagenomic sequencing data for rapid outbreak investigation and novel pathogen discovery.Frontiers in microbiology · 2025Review
- Multi-modal AI in precision medicine: integrating genomics, imaging, and EHR data for clinical insights.Frontiers in artificial intelligence · 2025Review
Corrections and comments
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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.
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