ReviewScience in One Health2023
Innovative applications of artificial intelligence in zoonotic disease management.
Review in Science in One Health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges of artificial intelligence in public health: a systematic review on technological efficacy, ethical dilemmas, and governance pathways.Frontiers in public health · 2025Pooled it
- Climate change impact on disease emergence and antimicrobial resistance: A perspective on transmission and detection.iScience · 2026Review
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Surveillance and response to emerging and re-emerging zoonotic diseases: advancing One Health from evidence to action.Science in One Health · 2026Article
- Advances and emerging technologies in the diagnosis of viral infections in pigs: Progress, challenges, and One Health perspectives.Veterinary world · 2025Review
- Artificial Intelligence Driven Framework for the Design and Development of Next-Generation Avian Viral Vaccines.Microorganisms · 2025Review
- Advancing AMR Surveillance: Confluence of One Health and Big Data Integration : Converging One Health and Big Data for AMR.EcoHealth · 2025Review
- One Health strategies in combating antimicrobial resistance: a Southeast Asian perspective.Journal of global health · 2025Review
- Biosecurity Implications, Transmission Routes and Modes of Economically Important Diseases in Domestic Fowl and Turkey.Veterinary sciences · 2025Review
- Celebrating three years ofScience in One Health · 2025Article
- Preparedness of the Local Population for the Uptake of Artificial Intelligence and Digital One Health for Home Healthcare of Emerging and Reemerging Infectious Diseases in Southwest and Littoral Regions of Cameroon.Journal of tropical medicine · 2025Article
- Advancing One Health implementation and technological innovation in China: a technology assessment approach.Science in One Health · 2025Review
- U-FDL-PPE: a unified federated deep learning framework with privacy-preserving explainability for early and accurate viral disease prediction.Frontiers in radiology · 2025Article
- Innovative applications of artificial intelligence during the COVID-19 pandemic.Infectious medicine · 2024Review
- The potential application of artificial intelligence in veterinary clinical practice and biomedical research.Frontiers in veterinary science · 2024Article
- Advances in Microbial Diagnostics: Machine Learning and Nanotechnology for Zoonotic Disease Control.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.
Indexed as
Identifiers
What Socratic 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 Socratic graph.