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Artificial intelligence and antimicrobial resistance in Bacteria
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Monireh Rahimkhani1    , Maryam Gilani , Arya Daneshvar |
| 1- , rrahimkhani@sina.tums.ac.ir |
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Abstract: (31 Views) |
Introduction and Objectives: Antibiotic resistance has increased significantly in recent years, and on the other hand, machine learning (ML) algorithms are increasingly used in medical research and healthcare and gradually improve clinical performance.
Material and Methods: Among the various applications of these new methods, their use in the fight against antimicrobial resistance (AMR) is one of the most critical areas of interest, because the rise of antibiotic resistance and the management of multidrug-resistant infections that are difficult to treat are important challenges.
Results: Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance and thus support clinicians in selecting the appropriate treatment.
Conclusion: Machine learning and (AI) in connection with the prediction of antimicrobial resistance is one of today's sciences , therefore, an antimicrobial stewardship program (ASP) should be implemented to optimize antibiotic prescribing through evidence-based clinical decisions, and to limit AMR.
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| Keywords: antimicrobial resistance, machine learning, artificial intelligence |
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Type of Study: Review |
Subject:
Microbiology
Received: 2024/12/22 | Accepted: 2026/08/5
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