TY - JOUR
T1 - A review of artificial intelligence applications for antimicrobial resistance
AU - Lv, Ji
AU - Deng, Senyi
AU - Zhang, Le
N1 - Funding Information: This research has received funding support from National Science and Technology Major Project [ 2018ZX10201002 ]. Publisher Copyright: © 2021
PY - 2021/2/1
Y1 - 2021/2/1
N2 - The wide use and abuse of antibiotics could make antimicrobial resistance (AMR) an increasingly serious issue that threatens global health and imposes an enormous burden on society and the economy. To avoid the crisis of AMR, we have to fundamentally change our approach. Artificial intelligence (AI) represents a new paradigm to combat AMR. Thus, various AI approaches to this problem have sprung up, some of which may be considered successful cases of domain-specific AI applications in AMR. However, to the best of our knowledge, there is no systematic review illustrating the use of these AI-based applications for AMR. Therefore, this review briefly introduces how to employ AI technology against AMR by using the predictive AMR model, the rational use of antibiotics, antimicrobial peptides (AMPs) and antibiotic combinations, as well as future research directions.
AB - The wide use and abuse of antibiotics could make antimicrobial resistance (AMR) an increasingly serious issue that threatens global health and imposes an enormous burden on society and the economy. To avoid the crisis of AMR, we have to fundamentally change our approach. Artificial intelligence (AI) represents a new paradigm to combat AMR. Thus, various AI approaches to this problem have sprung up, some of which may be considered successful cases of domain-specific AI applications in AMR. However, to the best of our knowledge, there is no systematic review illustrating the use of these AI-based applications for AMR. Therefore, this review briefly introduces how to employ AI technology against AMR by using the predictive AMR model, the rational use of antibiotics, antimicrobial peptides (AMPs) and antibiotic combinations, as well as future research directions.
KW - Antimicrobial resistance
KW - Artificial intelligence
KW - Clinical decision support systems
KW - Drug combinations
KW - Whole-genome sequencing
UR - http://www.scopus.com/inward/record.url?scp=85100771514&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.bsheal.2020.08.003
DO - https://doi.org/10.1016/j.bsheal.2020.08.003
M3 - Review article
SN - 2590-0536
VL - 3
SP - 22
EP - 31
JO - Biosafety and Health
JF - Biosafety and Health
IS - 1
ER -