ABSTRACT
Artificial intelligence has made significant advancements in the past few years, and its applications have expanded into various fields, including healthcare. Antimicrobial research is one of the areas in which AI is playing a crucial role, providing new solutions to the global challenge of antimicrobial resistance. Antimicrobial resistance is a natural process by which microbes develop the ability to survive against the drugs designed to kill them and grow continuously. Antimicrobial resistance has the potential to affect people at any stage of life, and it is one of the most challenging problems in the modern era. The use of antimicrobials has given rise to antimicrobial resistance, which is a serious issue that threatens global health and imposes an enormous burden on society as well as the economy. Thus, rapid and accurate antimicrobial resistance diagnostic methods are urgently needed. Timely identification of antimicrobial resistance improves patient prognosis and reduces inappropriate antimicrobial use. Artificial intelligence, including machine learning and deep learning, is an efficient approach to resolve this issue without expending much money and time. Due to the exponential growth of experimental and clinical data, the application of machine learning in the field of antimicrobial research has increased in the past few years.
In this chapter, we focus on the current state of research at the intersection of machine learning and antimicrobial research. We highlight the basic principles and features of available machine learning tools, the trends and future developments of machine learning in antimicrobial research, and the misuse of antibiotics in COVID-19. Although the use of machine learning for understanding, diagnosing, treating, and preventing antimicrobial resistance is still in its infancy, the continued growth of data and interest ensures that it will become an important tool for future translational research programs and help reduce the burden of antimicrobial resistance worldwide.
