Antimicrobial resistance detection technologies and the role of healthcare workforce: A scoping review of recent research.

Authors

Farkad Bantun, Naif A Jalal, Hani Faidah, Ahmad O Babalghith, Ayman K Johargy, Faraz Ahmad, Darin Mansor Mathkor, Shafiul Haque, Vaibhav Srivastava

Published in

Journal of infection and public health. Volume 19. Issue 12. Pages 103409. Oct 28, 2026. Epub Oct 28, 2026.

Abstract

Reliable determination of antimicrobial resistance (AMR) is fundamental to healthcare research and practice. High-quality research evidence is essential for guiding accurate AMR detection which, in turn, informs antimicrobial stewardship (AMS) decision-making. This scoping review is directed at a critical evaluation of the recent strategies in AMR detection across the globe.
Pertinent primary studies on AMR-associated strategies were identified by a comprehensive literature search on scholarly databases (PubMed, Google Scholar, and EMBASE) in accordance with the PRISMA-ScR guidelines. The final set of pertinent studies published during the last 5 years (2021-2026) included 58 studies. Subsequent analysis critically evaluated the evidence of integration of advanced technologies into AMR detection and prediction programs.
Whole-genome sequencing (WGS; n = 37 articles) was the primary method for AMR screening across the included studies. Metagenomic sequencing (MGS) data was used for AMR characterization in 6 studies, while PCR/multiplex PCR was used in 10 articles. An interesting trend was the use of machine learning (ML) models for predicting AMR. Almost all studies which developed, validated and/or employed ML models (n = 21 studies) reported excellent time- and cost-efficiency, high accuracy and precision, and good concordance with conventional methods for AMR screening. Building on this synthesis, we propose the Integrated Diagnostic-Stewardship-Workforce (IDSW) model, a hypothesis-generating, five layered conceptual framework linking diagnostic innovations, workforce-mediated decision making, AMS intervention, contextual modifiers, and outcome feedback loop.
The reviewed evidence supports the adaptation of AMR detection practices for clinical decision support systems, with potential downstream implications for technology-enhanced stewardship programs. Further, the proposed IDSW model offers a framework for guiding future research, policy making, and implementation efforts that integrate diagnostics and workforce roles, with AMS as a key downstream application.

PMID:
42966211
Bibliographic data and abstract were imported from PubMed on 01 Nov 2026.

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