The impact of artificial intelligence on the prescribing, selection, resistance, and stewardship of antimicrobials: a scoping review
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,019 | 0,101 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,021 | 0,020 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».