Yang JS, Wang Q, Lv ZW. Artificial intelligence for disease diagnostics still has a long way to go. World J Radiol 2024; 16(3): 69-71 [PMID: 38596172 DOI: 10.4329/wjr.v16.i3.69]
Corresponding Author of This Article
Jian-She Yang, MD, MSc, PhD, Academic Editor, Academic Fellow, Chairman, Chief Technician, Dean, Full Professor, Shanghai Tenth People's Hospital, Tongji University School of Medicine, No. 301 Yanchang Road (M), Shanghai 200072, China. 2305499@tongji.edu.cn
Research Domain of This Article
Computer Science, Artificial Intelligence
Article-Type of This Article
Letter to the Editor
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. Mar 28, 2024; 16(3): 69-71 Published online Mar 28, 2024. doi: 10.4329/wjr.v16.i3.69
Artificial intelligence for disease diagnostics still has a long way to go
Jian-She Yang, Qiang Wang, Zhong-Wei Lv
Jian-She Yang, Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
Jian-She Yang, Qiang Wang, Basic Medicine College, Gansu Medical College, Pingliang 744000, Gansu Province, China
Zhong-Wei Lv, Department of Nuclear Medicine, Shanghai Tenth People’s Hospital of Nanjing Medical University, Shanghai 200072, China
Author contributions: Yang JS, Wang Q, and Lv ZW designed the research, analyzed the data and wrote the paper.
Supported bythe Dean Responsible Project of Gansu Medical College, No. GY-2023FZZ01; University Teachers Innovation Fund Project of Gansu Province, No. 2023A-182; and Key Research Project of Pingliang Science and Technology, No. PL-STK-2021A-004.
Conflict-of-interest statement: All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential 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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jian-She Yang, MD, MSc, PhD, Academic Editor, Academic Fellow, Chairman, Chief Technician, Dean, Full Professor, Shanghai Tenth People's Hospital, Tongji University School of Medicine, No. 301 Yanchang Road (M), Shanghai 200072, China. 2305499@tongji.edu.cn
Received: January 4, 2024 Peer-review started: January 4, 2024 First decision: March 2, 2024 Revised: March 6, 2024 Accepted: March 14, 2024 Article in press: March 14, 2024 Published online: March 28, 2024 Processing time: 81 Days and 23.1 Hours
Abstract
Artificial intelligence (AI) can sometimes resolve difficulties that other advanced technologies and humans cannot. In medical diagnostics, AI has the advantage of processing figure recognition, especially for images with similar characteristics that are difficult to distinguish with the naked eye. However, the mechanisms of this advanced technique should be well-addressed to elucidate clinical issues. In this letter, regarding an original study presented by Takayama et al, we suggest that the authors should effectively illustrate the mechanism and detailed procedure that artificial intelligence techniques processing the acquired images, including the recognition of non-obvious difference between the normal parts and pathological ones, which were impossible to be distinguished by naked eyes, such as the basic constitutional elements of pixels and grayscale, special molecules or even some metal ions which involved into the diseases occurrence.
Core Tip: We strengthened the importance of mechanism elucidation of the advanced artificial intelligence in processing figures recognition, especially for those images with very similar characteristics that are difficult to be distinguished by the naked eye, and expressed a caution on decision making by using artificial intelligence technique for medical use, in that the unidentified potential would result in a bias.
