ARTIFICIAL INTELLIGENCE IN CLINICAL MICROBIOLOGY: IMPLICATIONS FOR POPULATION THERAPEUTICS, ANTIMICROBIAL STEWARDSHIP, AND PRECISION CLINICAL PHARMACOLOGY — A SYSTEMATIC REVIEW

Abstract

Artificial Intelligence (AI) is increasingly integrated into clinical microbiology, offering transformative potential for antimicrobial stewardship, therapeutic optimization, and population-level infectious disease management. This systematic review evaluates current evidence (2015–2025) on AI applications in microbiological diagnostics and their implications for clinical pharmacology and population therapeutics.

A structured search of PubMed, Scopus, and Web of Science identified studies assessing machine learning (ML), deep learning (DL), and predictive analytics applied to microscopy, culture interpretation, MALDI-TOF mass spectrometry, antimicrobial resistance (AMR) prediction, whole-genome sequencing (WGS), and clinical decision support systems. Eligible studies reported validated diagnostic or predictive performance metrics.

Among 136 included studies, AI-based image analysis achieved >95% accuracy in Gram stain interpretation and tuberculosis smear detection. Machine learning applied to MALDI-TOF spectra enabled early prediction of carbapenem resistance. Predictive models integrating microbiological data with electronic health records demonstrated strong performance in forecasting antimicrobial resistance (AUROC 0.82–0.95), optimizing empirical antibiotic therapy, and reducing inappropriate prescribing. AI-assisted sepsis prediction systems improved early antimicrobial initiation and clinical outcomes.

Despite promising results, challenges remain regarding dataset heterogeneity, external validation, algorithmic bias, regulatory oversight, and implementation in low-resource settings.

AI-driven clinical microbiology enhances therapeutic precision and strengthens antimicrobial stewardship at both individual and population levels. When ethically implemented and rigorously validated, AI has the potential to redefine pharmacological decision-making in infectious diseases and contribute significantly to global antimicrobial resistance mitigation strategies.