[Advances in integrated antimicrobial resistance diagnostics: quantitative, qualitative and AI-driven approaches]

The rapid global increase in antimicrobial resistance complicates the treatment of life-threatening infections and makes fast, reliable antimicrobial susceptibility testing (AST) essential. While phenotypic methods such as broth dilution, agar diffusion, gradient diffusion and automated systems remain the diagnostic standard, they are limited by long turnaround times. Rapid phenotypic AST (RAST) approaches shorten the time to first results to 4 to 8 h and allow earlier optimisation of anti-infective therapy, although their clinical benefit has not yet been conclusively demonstrated and their use is restricted to validated pathogens and substances.In parallel, molecular methods such as PCR, isothermal amplification and, increasingly, whole-genome sequencing enable rapid detection of key resistance determinants (e.g., mecA/C, vanA/B, extended-spectrum beta-lactamases [ESBL] and carbapenemase genes), thereby particularly supporting the workup of positive blood cultures and surveillance investigations. Their predictive value is high for Gram-positive pathogens but limited for Gram-negative organisms due to the diversity of resistance mechanisms. Artificial intelligence (AI) offers additional potential for automated interpretation of phenotypic tests, analysis of complex genomic data and mass-spectrometry-based resistance prediction models, but faces challenges regarding standardisation, generalisability and data quality.Overall, novel RAST, molecular and AI-supported approaches usefully complement but do not replace classical methods. Their clinical impact depends on targeted implementation and integration into effective antibiotic and diagnostic stewardship structures.