Editorial
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Cancer. Feb 28, 2021; 2(1): 1-6
Published online Feb 28, 2021. doi: 10.35713/aic.v2.i1.1
Cancer recognition of artificial intelligence
Shihori Tanabe
Shihori Tanabe, Division of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, Kanagawa, Japan
Author contributions: Tanabe S contributed to the writing and editing of the manuscript.
Supported by Japan Agency for Medical Research and Development (AMED), No. JP20ak0101093.
Conflict-of-interest statement: The author has nothing to disclose.
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: Shihori Tanabe, PhD, Senior Researcher, Division of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, 3-25-26, Tonomachi, Kawasaki-ku, Kawasaki 210-9501, Kanagawa, Japan. stanabe@nihs.go.jp
Received: February 12, 2021
Peer-review started: February 12, 2021
First decision: February 19, 2021
Revised: February 28, 2021
Accepted: February 28, 2021
Article in press: February 28, 2021
Published online: February 28, 2021
Abstract

The recognition mechanism of artificial intelligence (AI) is an interesting topic in understanding AI neural networks and their application in therapeutics. A number of multilayered neural networks can recognize cancer through deep learning. It would be interesting to think about whether human insights and AI attention are associated with each other or should be translated, which is one of the main points in this editorial. The automatic detection of cancer with computer-aided diagnosis is being applied in the clinic and should be improved with feature mapping in neural networks. The subtypes and stages of cancer, in terms of progression and metastasis, should be classified with AI for optimized therapeutics. The determination of training and test data during learning and selection of appropriate AI models will be essential for therapeutic applications.

Keywords: Artificial intelligence, Cancer, Network, Recognition, Therapeutic application

Core Tip: Recently, rapidly growing advances in deep learning have enabled cancer recognition by artificial intelligence (AI). Differences between human insights and AI attention may exist, and the interpretation of the modeling would lead to the further progression of AI-oriented therapeutics. The massive ability of AI is useful for cancer recognition.