Artificial Intelligence and the Silent Pandemic of Antimicrobial Resistance: A Comprehensive Exploration

  • Mohammed F. Al Marjani College of Science, Mustansiriyah University, Baghdad, Iraq
  • Rana K. Mohammed College of science, Baghdad university, Baghdad-Iraq
  • Ziad O. Ahmed Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad-Iraq
  • Yasmin Makki Mohialden Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad-Iraq
Keywords: Antibiotic Resistance (AMR), Machine Learning (ML), Drug Development Deep Learning (DL)


The rise of antimicrobial resistance (AMR) in the 21st century has made it a worldwide disaster. Due to the fast spread of AMR illnesses and the lack of novel antimicrobials, the silent pandemic is well known. This issue requires a fast and meaningful response, not just speculation. To address this dilemma, deep learning (DL) and machine learning (ML) have become essential in many sectors. As a cornerstone of modern research, machine learning helps handle the many aspects of AMR. AI helps researchers construct clinical decision-support systems by collecting clinical data. These methods enable antimicrobial resistance monitoring and wise use. Additionally, AI applications help research new drugs. AI also excels at synergistic medicine combinations, providing new treatment methods. This paper summarizes our extensive study of AI and the silent epidemic of antibiotic resistance. Through deep learning and machine learning applications across multiple dimensions, we hope to contribute to the proactive management of AMR, moving away from its presentation as a future problem to present-day solutions.


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How to Cite
Al Marjani, M. F., Mohammed, R. K., Ahmed, Z. O., & Mohialden, Y. M. (2024). Artificial Intelligence and the Silent Pandemic of Antimicrobial Resistance: A Comprehensive Exploration. Journal La Multiapp, 5(1), 25-37.