Tamam MO, Tamam MC. Artificial intelligence technologies in nuclear medicine. World J Radiol 2022; 14(6): 151-154 [PMID: 35978976 DOI: 10.4329/wjr.v14.i6.151]
Corresponding Author of This Article
Muge Oner Tamam, MD, Associate Professor, Department of Nuclear Medicine, Prof. Dr. Cemil Tascioglu City Hospital, Darulaceze cad., İstanbul 34381, Turkey. mugeoner@yahoo.com
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
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/
World J Radiol. Jun 28, 2022; 14(6): 151-154 Published online Jun 28, 2022. doi: 10.4329/wjr.v14.i6.151
Artificial intelligence technologies in nuclear medicine
Muge Oner Tamam, Muhlis Can Tamam
Muge Oner Tamam, Department of Nuclear Medicine, Prof. Dr. Cemil Tascioglu City Hospital, İstanbul 34381, Turkey
Muhlis Can Tamam, High School, Uskudar American Academy, İstanbul 34145, Turkey
Author contributions: Tamam MO performed the majority of the writing, prepared the figures and tables; Tamam MC performed data accusation and writing; Tamam MC provided the input in writing the paper; Tamam MC designed the outline and coordinated the writing of the paper.
Conflict-of-interest statement: There is no conflict of interest associated with the senior author or other coauthors who contributed their efforts to this manuscript.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Muge Oner Tamam, MD, Associate Professor, Department of Nuclear Medicine, Prof. Dr. Cemil Tascioglu City Hospital, Darulaceze cad., İstanbul 34381, Turkey. mugeoner@yahoo.com
Received: January 31, 2022 Peer-review started: January 31, 2022 First decision: April 8, 2022 Revised: April 20, 2022 Accepted: June 13, 2022 Article in press: June 13, 2022 Published online: June 28, 2022 Processing time: 147 Days and 15.9 Hours
Abstract
The use of artificial intelligence plays a crucial role in developing precision medicine in nuclear medicine. Artificial intelligence refers to a field of computer science aimed at imitating the performance of tasks typically requiring human intelligence. From machine learning to generative adversarial networks, artificial intelligence automized the workflow of medical imaging. In this mini-review, we encapsulate artificial intelligence models and their use in nuclear medicine imaging workflow.
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.
