From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health

Background

Antimicrobial resistance (AMR) is a leading global health threat requiring coordinated surveillance across human, animal, environmental, and genomic systems. Machine learning is increasingly applied to AMR data, yet its contribution to actionable surveillance intelligence, rather than prediction alone, remains poorly defined.