Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/859
Title: | Artificial Intelligence and Radiomics in Nuclear Medicine: Potentials and Challenges | Authors: | Aktolun, Cumali | Keywords: | Artificial intelligence Radiomics Machine learning Deep learning Artificial neural networks Supervised learning Unsupervised learning Radiology Future |
Publisher: | Springer | 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. | URI: | https://doi.org/10.1007/s00259-019-04593-0 https://hdl.handle.net/20.500.14365/859 |
ISSN: | 1619-7070 1619-7089 |
Appears in Collections: | PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
Show full item record
CORE Recommender
SCOPUSTM
Citations
27
checked on Mar 12, 2025
WEB OF SCIENCETM
Citations
23
checked on Mar 12, 2025
Page view(s)
66
checked on Mar 10, 2025
Download(s)
82
checked on Mar 10, 2025
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.