Publication Name	World Journal of Radiology
Manuscript ID	75501
Country	Türkiye

Received	2022-01-31 21:15
Peer-Review Started	2022-01-31 21:17
To Make the First Decision
Return for Revision	2022-04-08 08:47
Revised	2022-04-20 16:43
Second Decision	2022-06-06 03:23
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2022-06-17 22:58
Articles in Press	2022-06-17 22:58
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2022-06-22 09:08
Publish the Manuscript Online	2022-06-28 13:12

ISSN	1949-8470 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
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Category	Radiology, Nuclear Medicine & Medical Imaging
Manuscript Type	Minireviews
Article Title	Artificial intelligence technologies in nuclear medicine
Manuscript Source	Invited Manuscript
All Author List	Muge Oner Tamam and Muhlis Can Tamam
ORCID	Author(s) ORCID Number Muge Oner Tamam http://orcid.org/0000-0002-3793-0178 Muhlis Can Tamam http://orcid.org/0000-0002-1327-8947
Funding Agency and Grant Number
Corresponding Author	Muge Oner Tamam, MD, Associate Professor, Nuclear Medicine, Tamam, MO (reprint author), Prof. Dr. Cemil Tascioglu City Hospital, Istanbul, Turkey., Darulaceze cad., İstanbul 34381, Türkiye. mugeoner@yahoo.com
Key Words	Artificial intelligence; Machine learning; Deep learning; Artificial neural networks; Convolutional neural networks; Generative adversarial networks
Core Tip	Artificial intelligence is a distinguished tool for creating tailor-made medicine. Artificial intelligence (AI) consists of machine learning, deep learning, artificial neural networks, convolutional neural networks, and generative adversarial networks. These AI applications affect all phases of a routine medical imaging workflow in nuclear medicine: planning, image acquisition, and interpretation. The integration of AI into clinical workflow and protocols of medical imaging will provide the opportunity to decrease the error rate of physicians and eventually lead to improved patient management.
Publish Date	2022-06-28 13:12
Citation	Tamam MO, Tamam MC. Artificial intelligence technologies in nuclear medicine. World J Radiol 2022; 14(6): 151-154
URL	https://www.wjgnet.com/1949-8470/full/v14/i6/151.htm
DOI	https://dx.doi.org/10.4329/wjr.v14.i6.151

Full Article (PDF)	WJR-14-151.pdf
Full Article (Word)	WJR-14-151.docx
Manuscript File	75501_Auto_Edited-LJH-LS.docx
Answering Reviewers	75501-Answering reviewers.pdf
Audio Core Tip	75501-Audio core tip.m4a
Conflict-of-Interest Disclosure Form	75501-Conflict-of-interest statement.pdf
Copyright License Agreement	75501-Copyright license agreement.pdf
Non-Native Speakers of English Editing Certificate	75501-Language certificate.pdf
Peer-review Report	75501-Peer-review(s).pdf
Scientific Misconduct Check	75501-Bing-Liu JH-2.jpg
Scientific Misconduct Check	75501-CrossCheck.jpg
Scientific Editor Work List	75501-Scientific editor work list.pdf
CrossCheck Report	75501-CrossCheck report.pdf