  Antimicrobial resistance (AMR) is a growing public health threat, estimated
to cause over 10 million deaths per year and cost the global economy 100
trillion USD by 2050 under status quo projections. These losses would mainly
result from an increase in the morbidity and mortality from treatment failure,
AMR infections during medical procedures, and a loss of quality of life
attributed to AMR. Numerous interventions have been proposed to control the
development of AMR and mitigate the risks posed by its spread. This paper
reviews key aspects of bacterial AMR management and control which make
essential use of data technologies such as artificial intelligence, machine
learning, and mathematical and statistical modelling, fields that have seen
rapid developments in this century. Although data technologies have become an
integral part of biomedical research, their impact on AMR management has
remained modest. We outline the use of data technologies to combat AMR,
detailing recent advancements in four complementary categories: surveillance,
prevention, diagnosis, and treatment. We provide an overview on current AMR
control approaches using data technologies within biomedical research, clinical
practice, and in the "One Health" context. We discuss the potential impact and
challenges wider implementation of data technologies is facing in high-income
as well as in low- and middle-income countries, and recommend concrete actions
needed to allow these technologies to be more readily integrated within the
healthcare and public health sectors.
