AI-Driven Antimicrobial Stewardship and Prediction of Antimicrobial Resistance: Current Evidence, Clinical Applications, and Future Perspectives

Abstract

Background Antimicrobial resistance (AMR) threatens the effectiveness of antimicrobial therapy and increases the risk of treatment failure, prolonged hospitalization, and mortality. Antimicrobial stewardship programmes (ASPs) seek to optimize antimicrobial selection, dosing, route, and duration. Artificial intelligence (AI), including machine learning (ML) and natural language processing, may strengthen stewardship through resistance prediction, individualized empirical therapy, surveillance, and clinical decision support.

Objective To review current evidence, clinical applications, limitations, and future directions for AI-driven antimicrobial stewardship and AMR prediction.

Methods A narrative review approach was used to synthesize peer-reviewed studies, systematic reviews, clinical guidance, and authoritative surveillance resources on ML-based resistance prediction and stewardship. No formal systematic screening or quantitative synthesis was undertaken.

Results Retrospective studies show that ML models can estimate resistance risk from clinical history, prior microbiology, antimicrobial exposure, and local epidemiology. Personalized antibiogram approaches have demonstrated potential to maintain predicted coverage while reducing broad-spectrum prescribing in retrospective datasets. However, external validation, calibration, workflow integration, and prospective evidence of patient-centred benefit remain essential.

Conclusion AI can augment antimicrobial stewardship by providing timely, patient-specific risk estimates and helping prioritize treatment review. Predictions do not confirm susceptibility, and model performance does not by itself establish clinical benefit. Implementation should include prospective validation, multidisciplinary oversight, transparent uncertainty communication, and ongoing monitoring of clinical and microbiological outcomes.

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Cite this article

Ishan Mayur Bhai Patel. AI-Driven Antimicrobial Stewardship and Prediction of Antimicrobial Resistance: Current Evidence, Clinical Applications, and Future Perspectives. ClinSphere Journals. 2026;1(1). https://doi.org/10.5281/zenodo.23792239

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