Copyright
©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 7, 2020; 26(37): 5617-5628
Published online Oct 7, 2020. doi: 10.3748/wjg.v26.i37.5617
Published online Oct 7, 2020. doi: 10.3748/wjg.v26.i37.5617
Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review
Miguel Jiménez Pérez, Rocío González Grande, UGC de Aparato Digestivo, Unidad de Hepatología-Trasplante Hepático, Hospital Regional Universitario de Málaga, Málaga 29010, Spain
Author contributions: Jiménez Pérez M and Grande RG contributed equally to this work.
Conflict-of-interest statement: Authors have 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: Miguel Jiménez Pérez, MD, PhD, Chief Doctor, UGC de Aparato Digestivo, Unidad de Hepatología-Trasplante Hepático, Hospital Regional Universitario de Málaga, Avenida Carlos Haya, Málaga 29010, Spain. mjimenezp@commalaga.com
Received: July 8, 2020
Peer-review started: July 8, 2020
First decision: August 8, 2020
Revised: September 1, 2020
Accepted: September 18, 2020
Article in press: September 18, 2020
Published online: October 7, 2020
Processing time: 81 Days and 11.8 Hours
Peer-review started: July 8, 2020
First decision: August 8, 2020
Revised: September 1, 2020
Accepted: September 18, 2020
Article in press: September 18, 2020
Published online: October 7, 2020
Processing time: 81 Days and 11.8 Hours
Core Tip
Core Tip: The biological variability in the behavior of hepatocellular carcinoma (HCC), conditioned by multiple factors, makes it difficult to establish general standards of action applicable equally to all patients. Analysis of the vast amount of data now available and their relation with tumor behavior is fundamental to be able to establish an efficient approach to HCC. It is here that the computational power of artificial intelligence can play a determining role, though it is necessary to understand the strengths and limitations of this technology before it can be applied in clinical practice.