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Artif Intell Gastroenterol. Jul 28, 2020; 1(1): 5-11
Published online Jul 28, 2020. doi: 10.35712/aig.v1.i1.5
Application of artificial intelligence in hepatology: Minireview
Ryota Masuzaki, Tatsuo Kanda, Reina Sasaki, Naoki Matsumoto, Kazushige Nirei, Masahiro Ogawa, Mitsuhiko Moriyama
Ryota Masuzaki, Tatsuo Kanda, Reina Sasaki, Naoki Matsumoto, Kazushige Nirei, Masahiro Ogawa, Mitsuhiko Moriyama, Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
Author contributions: Masuzaki R and Kanda T contributed to the conceptualization; Masuzaki R contributed to original draft preparation; Kanda T contributed to reviewing and editing; Sasaki R, Matsumoto N, Ogawa M, Nirei K, and Moriyama M contributed to the supervision of this study; Moriyama M contributed to the project administration; all authors have read and agreed to the submitted version of the manuscript.
Conflict-of-interest statement: There is 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: Tatsuo Kanda, MD, PhD, Associate Professor, Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, 30-1 Oyaguchikami-cho, Itabashi-ku, Tokyo 173-8610, Japan. kanda2t@yahoo.co.jp
Received: May 28, 2020
Peer-review started: May 28, 2020
First decision: June 13, 2020
Revised: June 23, 2020
Accepted: July 16, 2020
Article in press: July 16, 2020
Published online: July 28, 2020
Processing time: 60 Days and 0.1 Hours
Abstract

With the rapid advancements in computer science, artificial intelligence (AI) has become an intrinsic part of our daily life and clinical practices. The concepts of AI, such as machine learning, deep learning, and big data, are extensively used in clinical and basic research. In this review, we searched for the articles in PubMed and summarized recent developments of AI concerning hepatology while focusing on the diagnosis and risk assessment of liver diseases. Ultrasound is widely conducted for the routine surveillance of hepatocellular carcinoma along with tumor markers. Computer-aided diagnosis is useful in the detection of tumors and characterization of space-occupying lesions. The prognosis of hepatocellular carcinoma can be estimated via AI using large-scale and high-quality training datasets. The prevalence of nonalcoholic fatty liver disease is increasing worldwide and pivotal concern in the field is who will progress and develop hepatocellular carcinoma. Most AI studies require a large dataset, including laboratory or radiological findings and outcome data. AI will be useful in reducing medical errors, supporting clinical decisions, and predicting clinical outcomes. Thus, cooperation between AI and humans is expected to improve healthcare.

Keywords: Artificial intelligence; Deep learning; Machine learning; Hepatocellular carcinoma; Prognosis; Computer-aided diagnosis

Core tip: Artificial intelligence (AI) plays a significant role in our daily life and the research field. In this review, we summarized the recent findings of AI concerning hepatology. AI will be useful in the detection and diagnosis of liver tumors and the discrimination of high-risk patients for hepatic decompensation and hepatocellular carcinoma development. Furthermore, AI can be utilized in basic research, such as in the interpretation of genomics, transcriptomics, and proteomics. We hope that this review will help in future management.