Editorial: Next-generation technologies for antibiotic susceptibility testing

Topic captures a field in dynamic flux, collectively demonstrating that the future of AST lies in transcending these limitations through a convergence of novel methodologies, clinical validation, and a deep engagement with practical implementation challenges.A dominant theme within this collection is the pursuit of rapid, accurate, and scalable AST platforms that bypass the bottlenecks of phenotypic culture. A paradigm shift is evident in the move from indirect genotyping to direct, real-time analysis. For instance, Jakubicek et al.introduce a transformative approach to resistance gene identification using a base calling-free hybrid transformer method for raw nanopore sequencing data. By circumventing the computational and error-prone step of base-calling, this technology accelerates the identification of AMR determinants, exemplifying a powerful trend: the application of deep Despite this wave of innovation, the path to widespread clinical integration is fraught with persistent challenges that temper enthusiasm. Several contributions in this collection critically address these hurdles. Technological complexity, prohibitive costs, and specialized infrastructure requirements remain formidable barriers, particularly for resource-limited or decentralized settings. Furthermore, the field continues to grapple with a lack of standardized regulatory validation for novel AST systems; clinical trust hinges on demonstrable concordance with established CLSI or EUCAST breakpoints (Cusack et al., 2019). A more profound scientific challenge lies in the genotype-phenotype correlation. Sequencing-based research underscores that the presence of a resistance gene does not always equate to phenotypic resistance, with factors like gene expression, regulatory networks, and epistatic interactions complicating predictions (Eladawy et al., 2025). This necessitates the development of curated databases and sophisticated predictive models that can reliably translate genomic data into robust clinical phenotypes.Looking forward, the trajectory for next-generation AST is becoming clear. First, the future lies in miniaturization and automation, pushing towards point-of-care (PoC) implementation through lab-on-a-chip devices and integrated optical detectors. Second, the most impactful solutions will likely be multi-modal, combining genotypic, phenotypic, and computational approaches to provide cross-validated, clinically actionable results. Third, establishing rigorous data standards and interoperability frameworks is non-negotiable for the field's success. The precision of genomic AST is only as reliable as the quality of its database curation and the efficacy of consensus interpretation frameworks. Ultimately, even the most revolutionary AST technology will fail to impact patient outcomes without parallel advances in regulatory support, sustainable reimbursement models, and comprehensive training for healthcare professionals.In conclusion, the articles within this Research Topic collectively paint a picture of a field on the cusp of transformation. From deep learning-powered sequencing analytics to rapid fluorogenic phenotyping, the tools to overcome the limitations of traditional AST are rapidly evolving. The collective enthusiasm and ingenuity on display foster genuine optimism that antimicrobial susceptibility testing is poised to become more rapid, intelligent, and capable of meeting the urgent demands of antimicrobial stewardship in the 21st century. The decisive challenge now lies not in invention alone, but in the concerted effort to standardize, democratize, and seamlessly integrate these powerful new tools into the fabric of global clinical practice.