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

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.

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Publication Details

Journal
npj Antimicrobials and Resistance
Published
2026-03-26
DOI
https://doi.org/10.1038/s44259-026-00192-w
Primary Topic
Pharmaceutical and Antibiotic Environmental Impacts
Type
article
Field-Weighted Citation Impact
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article

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

Ronan Adler Tavella, Jesús Rodríguez-Manzano, Sérgio Schenkman, Nilton Lincopán et al.
npj Antimicrobials and Resistance
Pharmaceutical and Antibiotic Environmental Impacts
article

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

Ronan Adler Tavella, Jesús Rodríguez-Manzano, Sérgio Schenkman, Nilton Lincopán, João Pedro Rueda Furlan, Eliana Guedes Stehling, José Luís Balcázar, Rodrigo Cayô, AC Gales, Zhi Chao Ruan, Fábio Parra Sellera, William Calero-Cáceres
article en

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.

npj Antimicrobials and ResistanceVol. 4(1)
Imperial College Healthcare NHS Trust (GB), Universidade Federal da Paraíba (BR), Universidade de São Paulo (BR), Catalan Institute for Water Research (ES), Sir Run Run Shaw Hospital (CN), Brf (Brazil) (BR), Zhejiang Environmental Monitoring Center (CN), Universidade Metropolitana de Santos (BR), Universidad Técnica de Ambato (EC), Imperial College London (GB), Universidade Federal de São Paulo (BR)
Openalex Percentile: Top 7%
Pharmaceutical and Antibiotic Environmental Impacts
DOIOpen Access PDFOpenAlex
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