AI-Driven Strategies for Combating Antimicrobial Resistance: Innovations in Rapid Diagnostics, Precision Treatment, and Phage Therapy

Abstract

Antimicrobial resistance (AMR) is an increasing global healthcare challenge that reduces the effectiveness of antibiotics and complicates the treatment of infectious diseases. Conventional microbiological diagnosis and antimicrobial susceptibility testing remain essential for identifying bacterial pathogens and selecting appropriate treatments; however, delays in obtaining results may contribute to inappropriate antimicrobial use. Artificial intelligence (AI), machine learning, genomic analysis, and advanced diagnostic technologies offer promising approaches to improving pathogen identification, resistance prediction, antimicrobial discovery, and clinical decision-making. This narrative review discusses the potential integration of AI with rapid diagnostic platforms, whole-genome sequencing, antimicrobial stewardship, and bacteriophage therapy. AI-based models can assist in analysing complex microbiological data, identifying resistance-associated patterns, and prioritising potential antimicrobial candidates. In parallel, bacteriophages offer a complementary therapeutic approach against selected bacterial infections, including infections caused by multidrug-resistant organisms. Nevertheless, limitations involving data quality, model generalisability, clinical validation, cost, regulatory oversight, and healthcare infrastructure remain significant barriers. The integration of AI with established microbiological methods could support faster and more informed treatment decisions when appropriately validated. This review highlights the need for interdisciplinary collaboration among microbiologists, clinicians, data scientists, and public health professionals to translate emerging technologies into accessible and clinically reliable AMR management strategies.