Artificial Intelligence in Predicting Antimicrobial Resistance Patterns: A Machine Learning Approach for Modern Microbiology

Abstract

Antimicrobial resistance (AMR) reduces antibiotic effectiveness and increases morbidity, mortality, and healthcare costs. Conventional antimicrobial susceptibility testing remains the clinical standard, but its turnaround time can delay treatment decisions. This review examines advances in artificial intelligence (AI) and machine learning (ML) for predicting resistance patterns in clinical microbiology. It synthesizes literature on the biological mechanisms of resistance, major ML algorithms, clinical and genomic data sources, predictive performance, clinical applications, and implementation challenges. The reviewed studies report high predictive performance for random forests, support vector machines, XGBoost, and deep neural networks applied to genomic and clinical datasets. AI-supported prediction systems could facilitate earlier targeted therapy, strengthen antimicrobial stewardship, reduce unnecessary use of broad-spectrum antibiotics, and support resistance monitoring. However, their implementation is constrained by data quality, limited model explainability, algorithmic bias, privacy concerns, and inadequate infrastructure. The review concludes that AI and ML offer promising approaches to AMR prediction, while their effective clinical use depends on rigorous validation, explainable models, multidisciplinary implementation, and equitable access to digital health infrastructure. By connecting predictive capabilities with implementation requirements, the review provides a basis for assessing the opportunities and limitations of AI-supported resistance prediction in contemporary microbiology.