"From Rapid Diagnostics to Antimicrobial Stewardship: Artificial Intelligence in the Clinical Microbiology Response to Antimicrobial Resistance".

Antimicrobial resistance (AMR) represents one of the most formidable public health challenges of the 21st century, threatening the efficacy of modern medicine across virtually every clinical domain. Artificial intelligence (AI) and machine learning (ML) technologies have emerged as transformative tools with the potential to fundamentally reshape how clinical microbiology laboratories and infectious diseases specialists approach the prevention and management of AMR. This review examines the current and emerging applications of AI across the AMR continuum, encompassing rapid pathogen identification, automated microscopy, antimicrobial susceptibility testing, antibiotic stewardship programs, epidemiological surveillance, and novel antibiotic discovery. Evidence from recent prospective studies and systematic reviews suggests that ML-integrated clinical decision support systems can meaningfully improve antibiotic prescribing accuracy and reduce unnecessary broad-spectrum antibiotic use. Conversely, large language models, while promising as assistants in clinical reasoning, currently demonstrate insufficient reliability for independent antimicrobial management decisions. Significant barriers to widespread adoption include data heterogeneity, algorithmic opacity, geographic generalizability, and ethical considerations surrounding patient data. Federated learning and explainable AI frameworks are emerging as critical enablers of responsible AI deployment in this context. Nevertheless, the convergence of whole-genome sequencing, real-time surveillance networks, reinforcement learning, and AI-driven analytics positions this field at an inflection point. This review aims to provide infectious diseases and clinical microbiology practitioners with a structured understanding of AI applications most relevant to AMR prevention, alongside a balanced appraisal of current limitations and future directions.