ArticleOne health outlook2025
Leveraging artificial intelligence for One Health: opportunities and challenges in tackling antimicrobial resistance - scoping review.
Article in One health outlook, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The use of artificial intelligence based modelling techniques in One Health-related infectious disease studies in Sub-Saharan Africa: a review.Frontiers in artificial intelligence · 2026Pooled it
- Artificial Intelligence-Powered One Health: A Predictive Framework for Managing Persistent Chemical Threats across Human, Animal, and Environmental Systems.Global challenges (Hoboken, NJ) · 2026Review
- Reimagining antimicrobial resistance: AI-driven predictive epidemiology and the C-AMRE framework for next-generation antibiotic discovery.The Journal of antibiotics · 2026Review
- Microbiome Engineering in Dairy Cattle: A Critical Review of Strategies for Disease Resistance, Productivity, and Sustainable Farming.Veterinary sciences · 2026Review
- Artificial intelligence for early detection and risk prediction of antimicrobial resistance in aquatic ecosystems.npj antimicrobials and resistance · 2026Review
- Antimicrobial Resistance inAntibiotics (Basel, Switzerland) · 2026Review
- Impact of Farm Management Practices onFoods (Basel, Switzerland) · 2026Review
- Leveraging Artificial Intelligence to Advance Bioinformatics in Africa: Opportunities, Challenges, and Ethical Considerations in Combating Antimicrobial Resistance.Bioinformatics and biology insights · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAntimicrobial resistance (AMR) is a global health challenge driven by the misuse of antimicrobials across humans, animals, and the environment, necessitating integrated One Health solutions.
objectiveThis scoping review aims to synthesise evidence on the opportunities and challenges of leveraging artificial intelligence (AI) to tackle AMR within the One Health framework.
methodsThis review adhered to the PRISMA-ScR guideline. A comprehensive literature search was conducted in PubMed, Embase, Scopus, Web of Science, along with citation searching and Google Scholar.
resultsA total of 543 studies were identified from these databases. After removing duplicates, 343 studies remained for screening. Following the title and abstract screening, 273 publications were selected for full-text review, and 43 studies were included in the final analysis. Studies written in English that explored the application of AI tools and techniques for AMR in any One Health domain were included. The review found that AI is widely applied to combat AMR across different sectors (human, animal, and environmental health), with key opportunities including the rapid identification of resistant pathogens, AI-powered surveillance and early warning, integration of diverse datasets, and support for drug discovery and antibiotic stewardship. However, significant challenges remain, such as data standardisation issues, limited model transparency, infrastructure and resource gaps, ethical and privacy concerns, and difficulties in real-world implementation and validation.
conclusionOverall, while AI has great potential to improve AMR management, fully realising its benefits will require investment in explainable AI, better data infrastructure, stronger cross-sector collaboration, and clear regulatory frameworks to ensure ethical and effective use within the One Health approach.
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.