Please use this identifier to cite or link to this item: http://elar.urfu.ru/handle/10995/117798
Title: Industry 4.0 and Digitalisation in Healthcare
Authors: Popov, V. V.
Kudryavtseva, E. V.
Katiyar, N. K.
Shishkin, A.
Stepanov, S. I.
Goel, S.
Issue Date: 2022
Publisher: MDPI
Citation: Industry 4.0 and Digitalisation in Healthcare / V. V. Popov, E. V. Kudryavtseva, N. K. Katiyar et al. // Materials. — 2022. — Vol. 15. — Iss. 6. — 2140.
Abstract: Industry 4.0 in healthcare involves use of a wide range of modern technologies including digitisation, artificial intelligence, user response data (ergonomics), human psychology, the Internet of Things, machine learning, big data mining, and augmented reality to name a few. The healthcare industry is undergoing a paradigm shift thanks to Industry 4.0, which provides better user comfort through proactive intervention in early detection and treatment of various diseases. The sector is now ready to make its next move towards Industry 5.0, but certain aspects that motivated this review paper need further consideration. As a fruitful outcome of this review, we surveyed modern trends in this arena of research and summarised the intricacies of new features to guide and prepare the sector for an Industry 5.0-ready healthcare system. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords: BIG DATA
DIGITALISATION
HEALTHCARE
INDUSTRY 4.0
INTERNET OF THINGS
ARTIFICIAL INTELLIGENCE
AUGMENTED REALITY
BIG DATA
DATA MINING
ERGONOMICS
HEALTH CARE
INTERNET OF THINGS
DIGITALIZATION
DIGITISATION
HEALTHCARE INDUSTRY
HEALTHCARE SYSTEMS
HUMAN PSYCHOLOGY
MODERN TECHNOLOGIES
PARADIGM SHIFTS
RESPONSE DATA
REVIEW PAPERS
USER COMFORTS
INDUSTRY 4.0
URI: http://elar.urfu.ru/handle/10995/117798
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85126956702
WOS ID: 000775033500001
PURE ID: 29929500
ISSN: 19961944
DOI: 10.3390/ma15062140
Sponsorship: CA15102, CA16235, CA18125, CA18224; European Association of National Metrology Institutes, EURAMET: EMPIR A185; UK Research and Innovation, UKRI: EP/L016567/1, EP/S013652/1, EP/S036180/1, EP/T001100/1, EP/T024607/1, EP/V026402/1; Royal Academy of Engineering, RAENG: IAPP18-19\295, TSP1332; Royal Society: NIF\R1\191571
Acknowledgments: We greatly acknowledge the financial support provided by the UKRI via Grants No. EP/L016567/1, EP/S013652/1, EP/S036180/1, EP/T001100/1 and EP/T024607/1, Transformation Foundation Industries NetworkPlus feasibility study award to LSBU (EP/V026402/1), the Royal Academy of Engineering via Grants No. IAPP18-19\295 and TSP1332, EURAMET EMPIR A185 (2018), the EU Cost Action (CA15102, CA18125, CA18224 and CA16235) and the Newton Fellowship award from the Royal Society (NIF\R1\191571). Wherever applicable, the work made use of Isambard Bristol, UK supercomputing service accessed by a Resource Allocation Panel (RAP) grant as well as ARCHER2 resources (Project e648).
Appears in Collections:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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