Machine Learning: A promising in-silico approach to curb antimicrobial resistance

ABSTRACT

Antimicrobial resistance (AMR) is a concern to public health, prompting the development of novel strategies for combating AMR. While the use of machine learning (ML) to AMR is in its infancy, it has made significant progress as a diagnosis tool, owing to the growing availability of phenotypic/genotypic datasets and much faster computational power. While applying ML in AMR research is viable, its use is limited. It has been used to predict antimicrobial susceptibility genotypes/phenotypes, discover novel antibiotics, and improve diagnosis when combined with spectroscopic and microscopy methods. ML implementation in healthcare settings has challenges to adoption due to concerns about model interpretability and data integrity. The focus of this review is to outline the significant benefits and drawbacks along with the salient trends reported in recent studies. © 2022 IEEE.