Artificial intelligence for early detection and risk prediction of antimicrobial resistance in aquatic ecosystems
Year: 2026
Type: article
Abstract: Aquatic environments are key reservoirs and dissemination pathways of antimicrobial resistance (AMR). However, current water-based surveillance remains fragmented and inefficient for the timely detection of emerging threats. Integrating artificial intelligence with embedded metadata provides a powerful pathway to identify novel antimicrobial resistance genes, characterize resistome profiles, and predict AMR dynamics in real-time by combining omics, environmental, and hydrological data into spatiotemporal predictive models. Successful implementation of this framework will require robust governance, ethical safeguards, and capacity building to support predictive AMR monitoring aligned with the One Health approach.
Institutions: Universidad Técnica de Ambato, Universidade Federal de São Paulo, Universidade de São Paulo, Universidade Metropolitana de Santos, Catalan Institute for Water Research, Imperial College Healthcare NHS Trust, Imperial College London, Sir Run Run Shaw Hospital, Zhejiang University, Universidade Federal da Paraíba
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,190
OpenAlex ID: W7140795189