Artificial intelligence for early detection and risk prediction of antimicrobial resistance in aquatic ecosystems

npj Antimicrobials and Resistance 2026 Open access

Read full text
Authors: William Calero-Cáceres, Ronan Adler Tavella, Fábio Parra Sellera, José Luís Balcázar, Jesús Rodríguez-Manzano, Rodrigo Cayô, Nilton Lincopán, AC Gales, Sérgio Schenkman, Zhi Chao Ruan, Eliana Guedes Stehling, João Pedro Rueda Furlan
DOI: 10.1038/s44259-026-00192-w
Cited by: 2
References: 65

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.

Fields of Study & Subjects

Pharmaceutical and Antibiotic Environmental Impacts Microbial Community Ecology and Physiology Fecal contamination and water quality

Publication details

Journal / source
npj Antimicrobials and Resistance
Publisher
Springer Science and Business Media LLC
Volume
4
Issue
1
Publication date
26 March 2026
Language
EN