Phenotypic Antimicrobial Susceptibility Testing and Genotypic Resistance Detection in Gram-Negative Bacteria: A Proposed Round-Trip Framework

Antimicrobial susceptibility testing (AST) is evolving from conventional culture-based methods toward rapid phenotypic, molecular, sequencing, and artificial intelligence-assisted approaches. This narrative review examines current and emerging strategies for Gram-negative bacteria and proposes a conceptual round-trip framework for integrating genotype, phenotype, and clinical outcome evaluation. Phenotypic AST remains the functional reference because it measures growth inhibition under standardized conditions and provides drug-specific minimum inhibitory concentrations or categorical results. Rapid phenotypic platforms can shorten time to actionable susceptibility information, whereas multiplex molecular panels provide earlier pathogen identification and detection of selected resistance mechanisms. Whole-genome, metagenomic, and long-read sequencing extend this information to resistance architecture, plasmid context, transmission, and surveillance, while validated machine-learning approaches may support drug-specific susceptibility prediction. However, genotype–phenotype discordance, incomplete resistance-gene coverage, workflow constraints, and limited evidence for consistent improvement in patient outcomes remain important limitations. The proposed model links early molecular information with phenotypic testing, discordance review, expert clinical interpretation, and governed investigational learning. Reference phenotypic AST supplies susceptibility labels; clinical outcomes are evaluated separately. External validation and prospective assessment are required before the proposed learning extension can support clinical care.