Publication Name	Artificial Intelligence in Medical Imaging
Manuscript ID	65324
Country	Türkiye

Received	2021-03-04 13:36
Peer-Review Started	2021-03-04 13:43
To Make the First Decision
Return for Revision	2021-03-14 23:59
Revised	2021-03-30 13:40
Second Decision	2021-04-19 07:02
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2021-04-20 05:07
Articles in Press	2021-04-20 05:07
Publication Fee Transferred
Edit the Manuscript by Language Editor	2021-04-26 01:34
Typeset the Manuscript	2021-04-26 10:17
Publish the Manuscript Online	2021-04-27 07:16

ISSN	2644-3260 (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/
Copyright	© The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
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Website	http://www.wjgnet.com

Category	Oncology
Manuscript Type	Minireviews
Article Title	Artificial intelligence in radiation oncology
Manuscript Source	Invited Manuscript
All Author List	Melek Yakar and Durmus Etiz
Funding Agency and Grant Number
Corresponding Author	Melek Yakar, MD, Assistant Professor, Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Büyükdere, Meselik Campus, Eskisehir 26040, Türkiye. mcakcay@ogu.edu.tr
Key Words	Radiation oncology; Radiotherapy; Artificial intelligence; Deep learning; Machine learning
Core Tip	Beginning with the initial patient interview, artificial intelligence (AI) can help predict post-treatment disease prognosis and toxicity. Additionally, AI can assist in the automated segmentation of both organs at risk and target volume, and the treatment planning process with advanced dose optimization. AI can optimize the quality control process and support a higher level of safety, quality, and maintenance efficiency.
Publish Date	2021-04-27 07:16
Citation	Yakar M, Etiz D. Artificial intelligence in radiation oncology. Artif Intell Med Imaging 2021; 2(2): 13-31
URL	https://www.wjgnet.com/2644-3260/full/v2/i2/13.htm
DOI	https://dx.doi.org/10.35711/aimi.v2.i2.13

Full Article (PDF)	AIMI-2-13.pdf
Full Article (Word)	AIMI-2-13.docx
Manuscript File	65324-Review-FilipodiaCL.docx
Answering Reviewers	65324-Answering reviewers.pdf
Audio Core Tip	65324-Audio core tip.m4a
Conflict-of-Interest Disclosure Form	65324-Conflict-of-interest statement.pdf
Copyright License Agreement	65324-Copyright license agreement.pdf
Non-Native Speakers of English Editing Certificate	65324-Language certificate.pdf
Peer-review Report	65324-Peer-review(s).pdf
Scientific Misconduct Check	65324-Scientific misconduct check.pdf
Scientific Editor Work List	65324-Scientific editor work list.pdf
CrossCheck Report	65324-CrossCheck report.pdf