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Cited by 34Antibiotic Use and ResistanceArtificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Artificial Intelligence to Improve Antibiotic Prescribing: A Systematic Review

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Amin, Doaa, Garzόn-Orjuela, Nathaly, Garcia Pereira, Agustin, Parveen, Sana, Vornhagen, Heike, Vellinga, Akke·2023·Antibiotics
DOI 10.3390/antibiotics12081293PMC PMC10451640
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Abstract

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).

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  • AI can streamline the drug prescription process.

    “Besides that, AI has shown the capability to streamline the drug prescription process. 31”

    From Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews (2024) · cites this paper as [31]

    The Discussion (section 4.1) reports that the reviewed AI studies improved aspects of antibiotic prescribing, including predicting inappropriate prescriptions. But the claim extends that finding to drug prescribing generally and implies a streamlined process; the Abstract says none of the studies evaluated implementation in clinical practice. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Authors
Amin, D., Garzόn-Orjuela, N., Garcia Pereira, A., Parveen, S., Vornhagen, H., & Vellinga, A.
Affiliations
  • University College Dublin (IE)
  • School of Public Health, Physiotherapy & Sports Science, University College Dublin, Belfield, Dublin 4, Dublin, Ireland
  • Insight Centre for Data Analytics, University of Galway, H91 AEX4 Galway, Ireland
  • Ollscoil na Gaillimhe – University of Galway (IE)
Corresponding author
Amin, Doaa
ORCID
Venue
Antibiotics
Publisher
MDPI AG
Published
2023-08-06
Volume
12
Issue
8
Pages
1293
ISSN
2079-6382 (ISSN-L)
DOI
10.3390/antibiotics12081293
PMC
PMC10451640
OpenAlex
W4385611393
Cited by
34
References
8
Citations by year
2026: 9 · 2025: 15 · 2024: 9 · 2023: 1
FWCI
0.66
Type
journal article
Language
en
Open access
Licence
Locations
  • Antibiotics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Research Leader Awards (RL) 2020, Health Research Board, Ireland (RL-20200-03)
Topics
  • Life Sciences › Immunology and Microbiology › Applied Microbiology and Biotechnology › Antibiotic Use and Resistance
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
artificial intelligence, antibiotic prescribing, antibiotic resistance, supervised learning, clinical decision support, clinical implementation, primary and secondary care

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