INTERNATIONAL JOURNAL OF EVIDENCE-BASED MEDICINE
Review Article
Sep 30, 2026 42 32

The Hidden AMR Gap: A Scoping Review and AI Framework for Detecting Under-Surveillance of Antimicrobial Resistance

Nkemdilim Clairelouise Okechukwu 1,
Olachi EMENYONU 2,
John Odunayo Adebayo 3,
Anita Efua Okpunu 4,
Bernardino Chinonye Onunogbo 5,
Koseyinoluwa Gbolahan Opabunmi 6 *
1 Department of Medical Microbiology Enugu State University Teaching Hospital, Parklane, Enugu, Nigeria2 Department of Nursing Stark State College, North Canton, OH, USA3 Department of Animal Sciences North Carolina Agricultural and Technical State University, Greensboro, NC, USA 4 Department of Medical Laboratory Science University of Benin,Edo State , Nigeria 5 Department of Medical Laboratory Science Imo State University, Owerri, Imo State , Nigeria 6 Department of Physiotherapy, Chrisland University, Abeokuta, Ogun State, Nigeria* Corresponding Author
International Journal of Evidence-Based Medicine, 1(3), 2026, jebm015, https://doi.org/10.63946/jebm/19463
Publication date: Sep 30, 2026
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ABSTRACT

Background: Antimicrobial resistance (AMR) surveillance is essential for estimating resistance burden and guiding public-health responses, yet coverage remains uneven across geographic regions, populations, healthcare settings, and One Health domains. Low observed resistance may therefore reflect limited detection and reporting rather than genuinely low resistance.
Objectives: To map the nature and determinants of AMR under-surveillance, examine computational approaches for identifying surveillance blind spots, and develop an evidence-informed framework for AI-enabled detection of hidden AMR surveillance gaps.
Methods: PubMed and Dimensions were systematically searched for literature published from 2015–2026. Eligible studies addressed AMR surveillance coverage, capacity, representativeness, completeness, or computational approaches relevant to surveillance gaps. Study selection and synthesis followed JBI scoping-review methodology and PRISMA-ScR guidance.
Results: Twenty-two studies were included. Under-surveillance encompassed geographic and population underrepresentation, diagnostic and laboratory limitations, fragmented information systems, delayed reporting, weak genomic coverage, and incomplete One Health integration. Computational approaches included spatial and statistical modelling, machine learning, geographic information systems, genomic analytics, digital laboratory systems, and environmental surveillance. These approaches could identify or partially address surveillance incompleteness but remained constrained by data quality and representativeness. The synthesis informed a framework for detecting discordance between expected AMR risk and observed surveillance intensity.
Conclusions: AMR under-surveillance is a systems-level problem that can distort the apparent distribution of resistance. AI should complement microbiological surveillance by identifying uncertainty and prioritizing settings requiring strengthened surveillance.

KEYWORDS

imicrobial Resistance AMR Surveillance Artificial Intelligence Machine Learning One Health Surveillance Gaps Global Health

CITATION (Vancouver)

Okechukwu NC, EMENYONU O, Adebayo JO, Okpunu AE, Onunogbo BC, Opabunmi KG. The Hidden AMR Gap: A Scoping Review and AI Framework for Detecting Under-Surveillance of Antimicrobial Resistance. International Journal of Evidence-Based Medicine. 2026;1(3):jebm015. https://doi.org/10.63946/jebm/19463
APA
Okechukwu, N. C., EMENYONU, O., Adebayo, J. O., Okpunu, A. E., Onunogbo, B. C., & Opabunmi, K. G. (2026). The Hidden AMR Gap: A Scoping Review and AI Framework for Detecting Under-Surveillance of Antimicrobial Resistance. International Journal of Evidence-Based Medicine, 1(3), jebm015. https://doi.org/10.63946/jebm/19463
Harvard
Okechukwu, N. C., EMENYONU, O., Adebayo, J. O., Okpunu, A. E., Onunogbo, B. C., and Opabunmi, K. G. (2026). The Hidden AMR Gap: A Scoping Review and AI Framework for Detecting Under-Surveillance of Antimicrobial Resistance. International Journal of Evidence-Based Medicine, 1(3), jebm015. https://doi.org/10.63946/jebm/19463
AMA
Okechukwu NC, EMENYONU O, Adebayo JO, Okpunu AE, Onunogbo BC, Opabunmi KG. The Hidden AMR Gap: A Scoping Review and AI Framework for Detecting Under-Surveillance of Antimicrobial Resistance. International Journal of Evidence-Based Medicine. 2026;1(3), jebm015. https://doi.org/10.63946/jebm/19463
Chicago
Okechukwu, Nkemdilim Clairelouise, Olachi EMENYONU, John Odunayo Adebayo, Anita Efua Okpunu, Bernardino Chinonye Onunogbo, and Koseyinoluwa Gbolahan Opabunmi. "The Hidden AMR Gap: A Scoping Review and AI Framework for Detecting Under-Surveillance of Antimicrobial Resistance". International Journal of Evidence-Based Medicine 2026 1 no. 3 (2026): jebm015. https://doi.org/10.63946/jebm/19463
MLA
Okechukwu, Nkemdilim Clairelouise et al. "The Hidden AMR Gap: A Scoping Review and AI Framework for Detecting Under-Surveillance of Antimicrobial Resistance". International Journal of Evidence-Based Medicine, vol. 1, no. 3, 2026, jebm015. https://doi.org/10.63946/jebm/19463

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