The impact of artificial intelligence on the prescribing, selection, resistance, and stewardship of antimicrobials: a scoping review
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".