Abstract
BACKGROUND: Antimicrobial selection, prescribing, and resistance are global health issues resulting from the overuse and misuse of antimicrobials in the healthcare and agricultural sectors. It raises healthcare costs, prolongs diseases, and escalates mortality. OBJECTIVE: The current study objective was to specifically explore how Artificial Intelligence and Machine Learning affect the selection of antimicrobials, address antimicrobial resistance, and strengthen antimicrobial stewardship programs through a structured scoping review. The aim was to clarify what direct impacts AI/ML have in these areas and how they contribute to improvements and challenges in practice. METHOD: A literature search was conducted in PubMed, Cochrane Library, Ovid Embase, Scopus, and CINAHL. A detailed search approach was developed to guarantee that all relevant studies were included. The entire electronic search strategy included terms such as “Artificial intelligence-AI,” “digital health,” “selection/prescribing of antimicrobials, “antimicrobial stewardship-AMS, “antimicrobial resistance-AMR, “Machine Learning-ML”, and “telemedicine,”. RESULTS: A critical appraisal of sources of evidence from the included studies was conducted using the Newcastle-Ottawa Quality Assessment Form. For this review, 70 sources related to artificial intelligence’s impact on antimicrobial selection/prescribing, resistance, and stewardship were initially screened. Of these, 33 were assessed for eligibility, resulting in 16 studies included in the review. Seventeen were excluded for lack of direct information relevant to AI’s effect on antimicrobial prescribing, resistance, and stewardship. This scoping review summarizes how artificial intelligence improves the accuracy of therapy selection, helps reduce inappropriate prescriptions by predicting necessity, and aids clinical decision-making (CDSS). It also details specific barriers, such as integration challenges, and facilitators like improved workflow, to incorporating artificial intelligence technologies in real-world clinical settings. CONCLUSION: The reviewed studies showed that Artificial Intelligence and Machine Learning improve selection, prescribing, antimicrobial resistance, and antimicrobial stewardship. The use of artificial intelligence and Machine Learning models in selection, prescribing, antimicrobial resistance, and antimicrobial stewardship has a profound impact on clinical outcomes. The utilization of Artificial Intelligence and Machine Learning enhances prescription accuracy in AMS programs. The use of Machine Learning optimizes antimicrobial selection and predicts resistance. Future research should examine the implementation of Artificial Intelligence, Machine Learning, and AI-CDSS over a more extended period to understand its long-term effects on professional practices and organizational structures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-025-12320-4.