| Artificial Intelligence in the Fight Against Antimicrobial Resistance: A Comprehensive Review of Predictive and Therapeutic Application |
| Paper ID : 1288-IGA |
| Authors |
|
Sara Najafi * Department of Biology, Faculty of Basic Sciences, Hamedan Branch, Islamic Azad University, Hamedan, Iran |
| Abstract |
| Background and Aim: Antimicrobial resistance (AMR) has emerged as a global health crisis, threatening the effectiveness of standard treatments for microbial infections. Conventional diagnostic and treatment methods are often slow, resource-intensive, and reactive rather than predictive. Recent advances in artificial intelligence (AI) offer promising avenues to address these limitations. However, there remains a lack of consolidated knowledge on how AI is currently being used to predict and treat AMR-related infections. This review aims to bridge this gap by evaluating the predictive and therapeutic applications of AI in the context of AMR. Methods: A systematic literature search was conducted using databases including PubMed, Scopus, and Web of Science. Keywords such as “artificial intelligence,” “machine learning,” “deep learning,” “antimicrobial resistance,” “drug-resistant infections,” and “microbial diseases” were used. Studies published in English from 2020 to 2025 were included. Selected articles employed AI techniques for either prediction (e.g., resistance profiling, outbreak forecasting) or treatment (e.g., drug discovery, optimization of therapy). Results: AI tools such as random forest classifiers, deep neural networks, and natural language processing algorithms have been successfully applied to predict resistance patterns, identify resistance genes, and recommend personalized antimicrobial therapies. In therapeutic applications, AI has accelerated the discovery of novel antimicrobials and optimized treatment regimens by analyzing complex biological and clinical datasets. Conclusion: AI has demonstrated significant potential in both the prediction and treatment of AMR-related infections. Continued integration of AI into microbial healthcare systems could transform the landscape of infectious disease management and improve patient outcomes globally. |
| Keywords |
| Antimicrobial Resistance (AMR), AI, Machine Learning, Predictive Modeling, Infectious Disease Management |
| Status: Accepted |
