Optimization of Antibiotic Therapy Protocols Using AI: A Comprehensive Review – C. Sebbar

Abstract

Antimicrobial resistance (AMR) poses an imminent threat to global public health, with projections estimating up to 10 million deaths annually by 2050. Traditional antibiotic therapy is hampered by delays in pathogen identification, reliance on broad-spectrum empiric treatments, and a sluggish drug development pipeline. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in antimicrobial stewardship (AMS). These technologies offer the promise of real-time data-driven interventions that personalize antibiotic therapy, reduce the selection pressure for resistance, and enhance clinical outcomes. This review examines the role of AI in analyzing large-scale patient data—including electronic health records (EHRs), microbiological profiles, and genomic information—to guide antibiotic selection, predict resistance patterns, and improve diagnostic speed. We discuss state-of-the-art ML models (e.g., decision trees, random forests, extreme gradient boosting, support vector machines, and neural networks) and their clinical applications. Furthermore, we analyze the operational, ethical, and technical challenges that must be addressed to ensure safe and effective integration of AI in AMS. Finally, we propose future research directions, including model interpretability, longitudinal validation, and the development of federated learning frameworks to ensure equitable access to AI-driven healthcare solutions.

Keywords: antimicrobial resistance; antimicrobial stewardship; artificial intelligence; machine learning; personalized antibiograms; diagnostic innovation; predictive models; public health

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