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Artif Intell Gastroenterol. Oct 28, 2022; 3(4): 96-104
Published online Oct 28, 2022. doi: 10.35712/aig.v3.i4.96
Role of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma
Rajesh Kumar Mokhria, Jasbir Singh
Rajesh Kumar Mokhria, Government Model Sanskriti Senior Secondary School, Chulkana, 132101, Panipat, Haryana, India
Jasbir Singh, Department of Biochemistry, Kurukshetra University, Kurukshetra, 136119, Haryana, India
Author contributions: Mokhria RK designed the outline, performed data acquisition, contributed to the majority of the writing, and proofread the paper; Singh J coordinated the writing of the paper and proofread the paper.
Conflict-of-interest statement: There is no conflict of interest associated with any of the senior authors 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: Rajesh Kumar Mokhria, PhD, Biology Lecturer, Government Model Sanskriti Senior Secondary School, Chulkana, Panipat 132101, Haryana, India. mokhria79@gmail.com
Received: June 7, 2022
Peer-review started: June 7, 2022
First decision: July 14, 2022
Revised: July 30, 2022
Accepted: September 13, 2022
Article in press: September 13, 2022
Published online: October 28, 2022
Abstract

Artificial intelligence (AI) evolved many years ago, but it gained much advancement in recent years for its use in the medical domain. AI with its different subsidiaries, i.e. deep learning and machine learning, examine a large amount of data and performs an essential part in decision-making in addition to conquering the limitations related to human evaluation. Deep learning tries to imitate the functioning of the human brain. It utilizes much more data and intricate algorithms. Machine learning is AI based on automated learning. It utilizes earlier given data and uses algorithms to arrange and identify models. Globally, hepatocellular carcinoma is a major cause of illness and fatality. Although with substantial progress in the whole treatment strategy for hepatocellular carcinoma, managing it is still a major issue. AI in the area of gastroenterology, especially in hepatology, is particularly useful for various investigations of hepatocellular carcinoma because it is a commonly found tumor, and has specific radiological features that enable diagnostic procedures without the requirement of the histological study. However, interpreting and analyzing the resulting images is not always easy due to change of images throughout the disease process. Further, the prognostic process and response to the treatment process could be influenced by numerous components. Currently, AI is utilized in order to diagnose, curative and prediction goals. Future investigations are essential to prevent likely bias, which might subsequently influence the analysis of images and therefore restrict the consent and utilization of such models in medical practices. Moreover, experts are required to realize the real utility of such approaches, along with their associated potencies and constraints.

Keywords: Hepatocellular carcinoma, Artificial intelligence, Deep learning, Machine learning, Support vector machines, Artificial neural networks

Core Tip: Globally, hepatocellular carcinoma is a major cause of illness and fatality. Although substantial progress has been made in the treatment strategy for hepatocellular carcinoma, managing it is still a major issue. Artificial intelligence in the area of gastroenterology, especially in hepatology, is particularly useful for various investigations of hepatocellular carcinoma because it is a commonly found tumor and has specific radiological features that enable diagnostic procedures without the requirement of histological study. Artificial intelligence is utilized to diagnose, curative and prediction goals.