ReviewCureus2025
The Role and Limitations of Artificial Intelligence in Combating Infectious Disease Outbreaks.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Powassan Virus in the United States: An AI-Integrative One Health Scoping Review of Ecological Drivers, Zoonotic Interactions, Symptom Profiles, and Public Health Implications.Infectious disease reports · 2026Review
- Emerging and Re-Emerging Viral Infections in Poultry: Integrating Traditional and AI-Based Control Strategies.Current microbiology · 2026Review
- Feasibility of AI-driven disease surveillance systems at international airports in sub-Saharan Africa: a narrative review.Frontiers in public health · 2026Review
- Article
- Filling the gap: artificial intelligence-driven one health integration to strengthen pandemic preparedness in resource-limited settings.Frontiers in public health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has emerged as a transformative tool in the management of pandemics, significantly enhancing disease prediction, diagnostics, drug discovery, and vaccine development. This manuscript explores AI's multifaceted applications during infectious disease outbreaks, from predictive modeling and outbreak forecasting to the acceleration of vaccine development and antimicrobial resistance detection. AI-driven technologies, including deep learning and reinforcement learning, have shown remarkable effectiveness in improving diagnostic accuracy, streamlining drug discovery processes, and providing real-time decision-making support for healthcare providers. However, despite its substantial contributions, the deployment of AI in pandemic management faces key limitations, including concerns about data privacy, model transparency, and the need for constant updates to adapt to emerging pathogens. The integration of AI with human expertise is essential to optimize global health outcomes and address these challenges. This review highlights both the potential and the obstacles to fully leveraging AI in pandemic response, proposing pathways for overcoming current limitations and maximizing AI's impact on future outbreaks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.