Antibiotic resistance (AR) has become a significant worldwide public health concern in the twenty-first century. Antimicrobial resistance (AMR) occurs when microorganisms, such as bacteria, fungi, parasites, and viruses, acquire genetic changes that make them resistant to antimicrobial drugs, including antibiotics. AMR, often known as the "Silent Pandemic," requires prompt and persistent intervention instead of postponement. Failure in preventative measures will result in AMR being the primary cause of mortality worldwide. In the fight against multidrug-resistant bacteria to halt antibiotic resistance, conventional techniques for developing drugs are expensive and take a long time, however AI systems can rapidly scan through extensive chemical libraries and forecast possible antibacterial substances. Considering the sluggish progress of ongoing antibiotic research, it is essential to accelerate the advancement of novel antibiotics and supplementary treatments. The acceleration is essential for effectively tackling the increasing health risk caused by antibiotic-resistant bacteria, so guaranteeing that we maintain an advantage in combating these developing threats. The use of AI in medical research has significant promise, particularly in addressing multidrug-resistant (MDR) infections to battle AMR. This study focuses on the effective applications of AI in addressing AMR and its potential to benefit humanity. It covers the fundamental concepts of AI, the resources now available for AI, its uses and scope, as well as its benefits and limits.AI algorithms also consistently observe antibiotic usage, occurrences of diseases, and trends of resistance. This review examines the AI to identify AMR markers, diagnosis in AMR, small molecule antibiotic development and also emphasizes emerging research domains, such as AMR detection and novel medication development, that contribute to the management of AMR.
Hatim et al. (Sun,) studied this question.