The Role of Machine Learning in Addressing Antibiotic Resistance: A New Era in Infectious Disease Control

Abstract

Antimicrobial resistance (AMR) refers to the ability of microorganisms, such as bacteria and viruses, to resist antimicrobial medications. Current strategies for addressing this escalating issue are often labor-intensive and costly. However, advancements in artificial intelligence (AI) are enabling the rapid evaluation of extensive chemical libraries and the prediction of novel antimicrobial compounds. AI holds significant promise for medical research in treating multidrug-resistant infections by enhancing the development of new medications through the analysis of antibiotic usage, disease prevalence, and resistance patterns. The use of AI has the potential to greatly benefit research by accelerating the discovery of new antibiotics that effectively combat antibiotic-resistant microbes. Predicting trends in antibiotic resistance through the examination of large data sets by AI systems may pave the way for the creation of preventative medicines. The speed and accuracy with which AI can evaluate data are revolutionizing how scientists develop new medicines, assess potential health concerns, and find ways to prevent illness. AMR is a growing concern, and AI is playing an increasingly crucial role in this battle. Medical research utilizing AI has tremendous potential in the ongoing fight against antibiotic resistance. This review examines how AI aids in AMR diagnosis, small molecule drug development, and the detection of AMR symptoms. Further research into AMR detection and the creation of novel medications are two areas that could prove valuable in treating antimicrobial resistance.

Keywords: antimicrobial resistance; artificial intelligence; bacteria; drugs; models.