Is it possible to identify genotypes underlying resistant phenotypes in Gram-negative pathogens?

PURPOSE OF REVIEW: This review explores the relationship between genotypes and resistant phenotypes in Gram-negative pathogens. We analyse to what extent conventional phenotypic methods predict genetic mechanisms of resistance, the reliability of genotypic approaches, and how integrated strategies may improve diagnostic accuracy and clinical utility. RECENT FINDINGS: Traditional AST remains the clinical reference standard due to its correlation with therapeutic outcomes, yet it often fails to identify the molecular basis of resistance. Molecular methods such as PCR, microarrays, and targeted sequencing allow rapid detection of known genes but cannot reliably predict expression or functionality. Whole-genome sequencing provides the most comprehensive overview, capturing both known and novel resistance determinants as well as mobile genetic elements. Nevertheless, genotype-phenotype discordance persists, driven by regulatory mutations, inducible expression, or synergistic mechanisms. Emerging technologies - including real-time sequencing, metagenomics, and machine learning-based predictive models - are enhancing our ability to infer phenotypes from genomic data. Still, these approaches face challenges of standardization, validation, and integration into clinical workflows. SUMMARY: Linking genotypes to resistant phenotypes in Gram-negative pathogens remains complex. While phenotypic AST ensures reliability for therapy, genotypic methods provide unprecedented insight into resistance mechanisms and epidemiology. Discrepancies between the two highlight the need for integrated diagnostic platforms that combine functional and genomic perspectives. Artificial intelligence-driven predictive models and curated resistance databases hold promise for improving accuracy, but widespread adoption requires robust datasets, clinical validation, and harmonized interpretative frameworks. Ultimately, integrating phenotypic and genotypic data represents the most effective strategy to provide mechanism-informed, clinically actionable diagnostics for antimicrobial resistance management.