Copyright
©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Med Imaging. Apr 28, 2021; 2(2): 13-31
Published online Apr 28, 2021. doi: 10.35711/aimi.v2.i2.13
Published online Apr 28, 2021. doi: 10.35711/aimi.v2.i2.13
Artificial intelligence in radiation oncology
Melek Yakar, Durmus Etiz, Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir 26040, Turkey
Melek Yakar, Durmus Etiz, Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey
Author contributions: Yakar M and Etiz D collected data and wrote the manuscript; Etiz D formatted and revised the article.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest.
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/
Corresponding author: Melek Yakar, MD, Assistant Professor, Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Büyükdere, Meselik Campus, Eskisehir 26040, Turkey. mcakcay@ogu.edu.tr
Received: March 4, 2021
Peer-review started: March 4, 2021
First decision: March 14, 2021
Revised: March 30, 2021
Accepted: April 20, 2021
Article in press: April 20, 2021
Published online: April 28, 2021
Processing time: 53 Days and 17.7 Hours
Peer-review started: March 4, 2021
First decision: March 14, 2021
Revised: March 30, 2021
Accepted: April 20, 2021
Article in press: April 20, 2021
Published online: April 28, 2021
Processing time: 53 Days and 17.7 Hours
Core Tip
Core Tip: Beginning with the initial patient interview, artificial intelligence (AI) can help predict posttreatment disease prognosis and toxicity. Additionally, AI can assist in the automated segmentation of both the organs at risk and target volumes and the treatment planning process with advanced dose optimization. AI can optimize the quality control process and support increased safety, quality, and maintenance efficiency.