Letter to the Editor
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Cancer. Aug 28, 2021; 2(4): 49-50
Published online Aug 28, 2021. doi: 10.35713/aic.v2.i4.49
How is artificial intelligence applied in solid tumor imaging?
Jian-She Yang, Qiang Wang
Jian-She Yang, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China
Jian-She Yang, Qiang Wang, Basic Medicine College, Gansu Medical College, Pingliang 744000, Gansu Province, China
Author contributions: Yang JS designed the research; Yang JS and Wang Q performed the research; Yang JS and Wang Q analyzed the data; Yang JS wrote and revised the letter.
Conflict-of-interest statement: The authors declare no conflicts of interest for 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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jian-She Yang, MSc, PhD, Academic Research, Professor, Shanghai Tenth People's Hospital, Tongji University, No. 301 Yanchang Road (M), Shanghai 200072, China. yangjs@impcas.ac.cn
Received: May 1, 2021
Peer-review started: May 1, 2021
First decision: June 18, 2021
Revised: June 21, 2021
Accepted: July 22, 2021
Article in press: July 22, 2021
Published online: August 28, 2021
Abstract

How is artificial intelligence (AI) applied in solid tumor imaging? What is the essential value of AI for tumor precision diagnosis and can it wholly replace the human beings? Some opinions in this letter should be considered.

Keywords: Artificial intelligence, Tumor, Imaging, Diagnosis

Core Tip: Artificial intelligence has been widely applied in tumor diagnosis due to its precise recognition and big-data handling properties, which can relieve the clinicians from the diagnostic workloads. However, this model, to some extent, is rigid, and cannot completely replace the human beings eventually. How to promote and optimize it with real intelligence has a long way to go.