Ip WY, Yeung FK, Yung SPF, Yu HCJ, So TH, Vardhanabhuti V. Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy. Artif Intell Med Imaging 2021; 2(2): 37-55 [DOI: 10.35711/aimi.v2.i2.37]
Corresponding Author of This Article
Varut Vardhanabhuti, BSc, MBBS, PhD, Assistant Professor, Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Room 406, Block K, Pok Fu Lam Road, Hong Kong SAR, China. varv@hku.hk
Research Domain of This Article
Oncology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Artif Intell Med Imaging. Apr 28, 2021; 2(2): 37-55 Published online Apr 28, 2021. doi: 10.35711/aimi.v2.i2.37
Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy
Wing-Yan Ip, Fu-Ki Yeung, Shang-Peng Felix Yung, Hong-Cheung Jeffrey Yu, Tsz-Him So, Varut Vardhanabhuti
Wing-Yan Ip, Shang-Peng Felix Yung, Varut Vardhanabhuti, Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
Fu-Ki Yeung, Medical Physics and Research Department, The Hong Kong Sanitorium & Hospital, Hong Kong SAR, China and Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
Hong-Cheung Jeffrey Yu, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
Tsz-Him So, Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
Author contributions: Ip WY, Yeung FK, Yung SPF, Yu HCJ, So TH and Vardhanabhuti V contributed to study design, review of literatures, interpretation of data, drafting and revision of the manuscript.
Conflict-of-interest statement: There is no conflict 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: Varut Vardhanabhuti, BSc, MBBS, PhD, Assistant Professor, Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Room 406, Block K, Pok Fu Lam Road, Hong Kong SAR, China. varv@hku.hk
Received: March 22, 2021 Peer-review started: March 22, 2021 First decision: March 26, 2021 Revised: April 1, 2021 Accepted: April 20, 2021 Article in press: April 20, 2021 Published online: April 28, 2021 Processing time: 36 Days and 4.6 Hours
Abstract
Artificial intelligence (AI) has seen tremendous growth over the past decade and stands to disrupts the medical industry. In medicine, this has been applied in medical imaging and other digitised medical disciplines, but in more traditional fields like medical physics, the adoption of AI is still at an early stage. Though AI is anticipated to be better than human in certain tasks, with the rapid growth of AI, there is increasing concerns for its usage. The focus of this paper is on the current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy. Topics on AI for image acquisition, image segmentation, treatment delivery, quality assurance and outcome prediction will be explored as well as the interaction between human and AI. This will give insights into how we should approach and use the technology for enhancing the quality of clinical practice.
Core Tip: Artificial intelligence (AI) applications in medical physics and radiotherapy represent an important emerging area in AI applications in medicine. The most notable improvements for the many aspects of radiotherapy are the ability to provide an accurate result with consistency and eliminate inter-and intra-observer variations. Perspectives from physicians and medical physicists about the use of AI are presented, and suggestions of how human can co-exist with AI are made to better equip us for the future.
