Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches

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.