Conference Abstract | Volume 9 (ConfPro8): Abstract 126 | Published: 15 Oct 2026
Christopher Effiong Essien1,&, and Uduak-Obong Akanimoh Uwaette1
1Medical Laboratory Department, Ibom Multi Specialty Hospital, Uyo, Nigeria
&Corresponding author: Christopher Effiong Essien, Medical Laboratory Department, Ibom Multi Specialty Hospital, Uyo, Nigeria, Email: chrees3662@gmail.com
Received: 11 Mar 2026, Accepted: 30 Aug 2026, Published: 15 Oct 2026
Domain: Infectious Disease Epidemiology
Keywords: Artificial intelligence, antimicrobial resistance, One Health, antimicrobial stewardship
©Christopher Effiong Essien et al. Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Christopher Effiong Essien et al., Artificial intelligence applications in One Health approaches to antimicrobial resistance: A systematic review. Journal of Interventional Epidemiology and Public Health. 2026;9(ConfPro8):126. https://doi.org/10.37432/JIEPH-CONFPRO8-00126
Antimicrobial resistance (AMR) is one of the greatest global public health threats, contributing to an estimated 4.95 million deaths annually worldwide (Murray et al., 2022). AMR is driven by inappropriate antimicrobial use in human medicine, veterinary practice, agriculture, and environmental contamination. The One Health approach, integrating human, animal, and environmental health, provides a comprehensive framework for addressing AMR. Artificial intelligence (AI) offers promising opportunities to strengthen AMR surveillance and antimicrobial stewardship (WHO, 2023). To systematically review applications of AI technologies in AMR surveillance, prediction, diagnosis, prevention, management, and antimicrobial discovery across human, animal, and environmental health sectors.
A systematic review was conducted following Preferred Reporting Items for Systematic Reviews (PRISMA 2020) guidelines. PubMed, Scopus, Web of Science, Embase, and IEEE Xplore were searched for studies published from January 2015 to March 2026. Search terms included “artificial intelligence,” “machine learning,” “deep learning,” “predictive analytics,” “antimicrobial resistance,” “antibiotic resistance,” and “One Health.” Eligible studies evaluated AI applications for AMR surveillance, diagnostics, antimicrobial stewardship, resistance prediction, or antimicrobial discovery in human, veterinary, agricultural, or environmental settings.
The search identified 2,146 records, of which 58 studies met the inclusion criteria. Of these, 20 (34.5%) focused on AMR surveillance, 16 (27.6%) on predictive modelling, 13 (22.4%) on rapid diagnostics, and 9 (15.5%) on antimicrobial discovery. AI-assisted diagnostic platforms reduced the time required for pathogen identification and susceptibility testing compared with conventional laboratory methods.
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