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dc.contributor.author
Zemmar, Ajmal
dc.contributor.author
Lozano, Andres M.
dc.contributor.author
Nelson, Bradley J.
dc.date.accessioned
2020-10-23T12:50:46Z
dc.date.available
2020-10-22T08:52:19Z
dc.date.available
2020-10-23T12:50:46Z
dc.date.issued
2020-10
dc.identifier.issn
2522-5839
dc.identifier.other
10.1038/s42256-020-00238-2
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/447204
dc.description.abstract
The COVID-19 pandemic has changed our world and impacted multiple layers of our society. All frontline workers and in particular those in direct contact with patients have been exposed to major risk. To mitigate pathogen spread and protect healthcare workers and patients, medical services have been largely restricted, including cancellation of elective surgeries, which has posed a substantial burden for patients and immense economic loss for various hospitals. The integration of a robot as a shielding layer, physically separating the healthcare worker and patient, is a powerful tool to combat the omnipresent fear of pathogen contamination and maintain surgical volumes. In this Perspective, we outline detailed scenarios in the pre-, intra- and postoperative care, in which the use of robots and artificial intelligence can mitigate infectious contamination and aid patient management in the surgical environment during times of immense patient influx. We also discuss cost-effectiveness and benefits of surgical robotic systems beyond their use in pandemics. The current pandemic creates unprecedented demands for hospitals. Digitization and machine intelligence are gaining significance in healthcare to combat the virus. Their legacy may well outlast the pandemic and revolutionize surgical performance and management. © 2020 Springer Nature Limited.
en_US
dc.language.iso
en
en_US
dc.publisher
Nature Publishing Group
en_US
dc.title
The rise of robots in surgical environments during COVID-19
en_US
dc.type
Journal Article
dc.date.published
2020-10-13
ethz.journal.title
Nature Machine Intelligence
ethz.journal.volume
2
en_US
ethz.journal.issue
10
en_US
ethz.journal.abbreviated
Nat Mach Intell
ethz.pages.start
566
en_US
ethz.pages.end
572
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems::03627 - Nelson, Bradley J. / Nelson, Bradley J.
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems::03627 - Nelson, Bradley J. / Nelson, Bradley J.
ethz.date.deposited
2020-10-22T08:52:32Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2020-10-23T12:50:58Z
ethz.rosetta.lastUpdated
2022-03-29T03:38:43Z
ethz.rosetta.versionExported
true
ethz.COinS
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