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@ARTICLE{Fritsch:907333,
author = {Fritsch, Sebastian and Sharafutdinov, Konstantin and
Schuppert, Andreas and Bickenbach, Johannes},
title = {{N}utzung von künstlicher {I}ntelligenz zur {B}ekämpfung
der {COVID}-19-{P}andemie},
journal = {Anästhesiologie, Intensivmedizin, Notfallmedizin,
Schmerztherapie},
volume = {57},
number = {03},
issn = {0174-1837},
address = {Stuttgart [u.a.]},
publisher = {Thieme},
reportid = {FZJ-2022-01970},
pages = {185 - 197},
year = {2022},
abstract = {The COVID-19 pandemic is a global health emergency of
historic dimension. In this situation, researchers worldwide
wanted to help manage the pandemic by using artificial
intelligence (AI). This narrative review aims to describe
the usage of AI in the combat against COVID-19. The
addressed aspects encompass AI algorithms for analysis of
thoracic X-rays or CTs, prediction models for severity and
outcome of the disease, AI applications in development of
new drugs and vaccines as well as forecasting models for
spread of the virus. The review shows, which approaches were
pursued, and which were successful.},
cin = {JSC},
ddc = {610},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
and Research Groups (POF4-511) / SMITH -
Medizininformatik-Konsortium - Beitrag Forschungszentrum
Jülich (01ZZ1803M)},
pid = {G:(DE-HGF)POF4-5112 / G:(BMBF)01ZZ1803M},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:35320841},
UT = {WOS:000821297100004},
doi = {10.1055/a-1423-8039},
url = {https://juser.fz-juelich.de/record/907333},
}