REVIEW article
Front. Microbiol.
Sec. Antimicrobials, Resistance and Chemotherapy
Volume 16 - 2025 | doi: 10.3389/fmicb.2025.1673343
This article is part of the Research TopicNext-Generation Technologies for Antibiotic Susceptibility TestingView all 7 articles
Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches
Provisionally accepted
Alexandru Constantin Aldea1
Filofteia Camelia Diguta1*
Oriana Presacan2
Catalina Voaides1
Radu Cristian Toma1
Florentina Matei1,3- 1Universitatea de Stiinte Agronomice si Medicina Veterinara din Bucuresti, Bucharest, Romania
- 2Universitatea Politehnica din Bucuresti Facultatea de Electronica Telecomunicatii si Tehnologia Informatiei, Bucharest, Romania
- 3Universitatea Transilvania din Brasov, Brașov, Romania
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Antibiotic resistance continues to erode the effectiveness of modern medicine, creating an urgent demand for rapid and reliable diagnostic solutions. Conventional diagnostic approaches, including culture-based susceptibility testing, remain the clinical reference standard but are constrained by lengthy turnaround times and limited sensitivity for early detection. In recent years, significant progress has been made with molecular and spectrometry-based methods, such as PCR and next-generation sequencing, MALDI-TOF MS, Raman and FTIR spectroscopy, alongside emerging CRISPR-based platforms. Complementary innovations in biosensors, microfluidics, and artificial intelligence further expand the diagnostic landscape, enabling faster, more sensitive, and increasingly portable assays. This review examines both established and emerging technologies for detecting antibiotic resistance, outlining their respective strengths, limitations, and potential roles across diverse settings. By synthesizing current advances and highlighting future opportunities, this review emphasizes complementarities among detection strategies and their potential integration into practical diagnostic frameworks, including in resource-limited settings.
Keywords: antibiotic resistance, pathogens, Detection methods, multidrug resistance, ESKAPE, Nanotechnological platforms, artificial intelligence, machine learning
Received: 25 Jul 2025; Accepted: 17 Sep 2025.
Copyright: © 2025 Aldea, Diguta, Presacan, Voaides, Toma and Matei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Filofteia Camelia Diguta, camelia.diguta@bth.usamv.ro
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.