Artificial Intelligence and Radiomics in Nuclear Medicine: Potentials and Challenges

dc.contributor.author Aktolun, Cumali
dc.date.accessioned 2023-06-16T12:47:45Z
dc.date.available 2023-06-16T12:47:45Z
dc.date.issued 2019
dc.description.abstract Artificial intelligence involves a wide range of smart techniques that are applicable to medical services including nuclear medicine. Recent advances in computer power, availability of accumulated digital archives containing large amount of patient images, and records bring new opportunities for the implementation of artificial techniques in nuclear medicine. As a subset of artificial intelligence, machine learning is an emerging tool that can possibly perform many clinical tasks. Nuclear medicine community needs to adapt to this fast approaching smart era, to exploit the opportunities and tackle the problems associated with artificial intelligence tools. It is aimed in this editorial to outline the opportunities and challenges of artificial intelligence applications in nuclear medicine. en_US
dc.identifier.doi 10.1007/s00259-019-04593-0
dc.identifier.issn 1619-7070
dc.identifier.issn 1619-7089
dc.identifier.scopus 2-s2.0-85074721035
dc.identifier.uri https://doi.org/10.1007/s00259-019-04593-0
dc.identifier.uri https://hdl.handle.net/20.500.14365/859
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation.ispartof European Journal of Nuclear Medıcıne And Molecular Imagıng en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Artificial intelligence en_US
dc.subject Radiomics en_US
dc.subject Machine learning en_US
dc.subject Deep learning en_US
dc.subject Artificial neural networks en_US
dc.subject Supervised learning en_US
dc.subject Unsupervised learning en_US
dc.subject Radiology en_US
dc.subject Future en_US
dc.title Artificial Intelligence and Radiomics in Nuclear Medicine: Potentials and Challenges en_US
dc.type Editorial en_US
dspace.entity.type Publication
gdc.author.id Aktolun, Cumali/0000-0002-6245-9349
gdc.author.scopusid 56356834500
gdc.author.wosid Aktolun, Cumali/K-8514-2018
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gdc.coar.access open access
gdc.coar.type text::journal::editorial
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gdc.description.department İzmir Ekonomi Üniversitesi en_US
gdc.description.departmenttemp [Aktolun, Cumali] Izmir Univ Econ, TR-35330 Izmir, Turkey en_US
gdc.description.endpage 2736 en_US
gdc.description.issue 13 en_US
gdc.description.publicationcategory Diğer en_US
gdc.description.scopusquality Q1
gdc.description.startpage 2731 en_US
gdc.description.volume 46 en_US
gdc.description.wosquality Q1
gdc.identifier.openalex W2982684882
gdc.identifier.pmid 31673788
gdc.identifier.wos WOS:000493503400002
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gdc.index.type PubMed
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gdc.oaire.impulse 16.0
gdc.oaire.influence 3.676371E-9
gdc.oaire.isgreen false
gdc.oaire.keywords Artificial Intelligence
gdc.oaire.keywords Image Processing, Computer-Assisted
gdc.oaire.keywords Humans
gdc.oaire.keywords Nuclear Medicine
gdc.oaire.keywords Molecular Imaging
gdc.oaire.popularity 2.3572987E-8
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gdc.oaire.sciencefields 03 medical and health sciences
gdc.oaire.sciencefields 0302 clinical medicine
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gdc.opencitations.count 25
gdc.plumx.mendeley 82
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gdc.plumx.scopuscites 35
gdc.scopus.citedcount 35
gdc.virtual.author Aktolun, CUMALİ
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