Artificial intelligence in antimicrobial stewardship: prediction, clinical applications, and implementation challenges.

BACKGROUND: Antimicrobial resistance (AMR) continues to threaten modern infectious diseases practice. Antimicrobial stewardship programs (ASPs) remain central to optimizing antimicrobial use, yet stewardship has become increasingly challenging because of rising clinical complexity, expanding data sources, and persistent workforce and analytic constraints. Artificial intelligence (AI) may strengthen stewardship by integrating clinical, microbiologic, and contextual data to support more timely and individualized decision-making. OBJECTIVES: To review current applications of AI in antimicrobial stewardship, with emphasis on resistance prediction and risk stratification, empiric therapy selection, diagnostic stewardship, antimicrobial optimization, and implementation challenges in clinical practice. SOURCES: Relevant studies evaluating AI and machine learning approaches for AMR prediction, diagnostic stewardship, antimicrobial optimization, and clinical implementation were reviewed. CONTENT: AI-based models have been developed to predict AMR and identify patients at risk for multidrug-resistant infections using electronic health record data. These approaches may support empiric therapy selection and patient-level risk stratification, although important methodological limitations remain, including heterogeneous prediction targets, data leakage, and limited external validation. AI applications also extend to diagnostic stewardship, including optimization of blood culture use, rationalization of molecular diagnostics, and support for interpretation of microbiologic results. In the therapeutic phase, AI may support de-escalation, intravenous-to-oral conversion, duration-of-therapy reassessment, and prioritization of stewardship review, aligning clinical decisions with antimicrobial stewardship goals. However, successful implementation depends not only on model performance but also on effective integration into clinical workflows, interpretability, governance, and clinician uptake. IMPLICATIONS: AI has the potential to enhance antimicrobial stewardship by enabling more precise, scalable, and workflow-integrated decision support. Future work should prioritize prospective and multicenter evaluation, careful implementation in routine care, and governance frameworks that address safety, transparency, equity, and clinician oversight. AI should be viewed as a tool to augment ASP expertise rather than replace clinical judgment.