Artificial Intelligence to Improve Antibiotic Prescribing: A Systematic Review

Introduction: The use of antibiotics leads to antibiotic resistance (ABR). Different methods have been used to predict and control ABR. In recent years, artificial intelligence (AI) has been explored to improve antibiotic (AB) prescribing, and thereby control and reduce ABR. This review explores whether the use of AI can improve antibiotic prescribing for human patients. Methods: Observational studies that use AI to improve antibiotic prescribing were retrieved for this review. There were no restrictions on the time, setting or language. References of the included studies were checked for additional eligible studies. Two independent authors screened the studies for inclusion and assessed the risk of bias of the included studies using the National Institute of Health (NIH) Quality Assessment Tool for observational cohort studies. Results: Out of 3692 records, fifteen studies were eligible for full-text screening. Five studies were included in this review, and a narrative synthesis was carried out to assess their findings. All of the studies used supervised machine learning (ML) models as a subfield of AI, such as logistic regression, random forest, gradient boosting decision trees, support vector machines and K-nearest neighbours. Each study showed a positive contribution of ML in improving antibiotic prescribing, either by reducing antibiotic prescriptions or predicting inappropriate prescriptions. However, none of the studies reported the engagement of AB prescribers in developing their ML models, nor their feedback on the user-friendliness and reliability of the models in different healthcare settings. Conclusion: The use of ML methods may improve antibiotic prescribing in both primary and secondary settings. None of the studies evaluated the implementation process of their models in clinical practices. Prospero Registration: (CRD42022329049).