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BACKGROUND: Antimicrobial resistance (AMR) is a global health challenge driven by the misuse of antimicrobials across humans, animals, and the environment, necessitating integrated One Health solutions. OBJECTIVE: This scoping review aims to synthesise evidence on the opportunities and challenges of leveraging artificial intelligence (AI) to tackle AMR within the One Health framework. METHODS: This 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. RESULTS: A 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. CONCLUSION: Overall, 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.
Kasse et al. (Thu,) studied this question.