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Artif Intell Gastroenterol. Nov 28, 2020; 1(4): 71-85
Published online Nov 28, 2020. doi: 10.35712/aig.v1.i4.71
Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives
Michihiro Kudou, Toshiyuki Kosuga, Eigo Otsuji
Michihiro Kudou, Toshiyuki Kosuga, Eigo Otsuji, Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
Michihiro Kudou, Department of Surgery, Kyoto Okamoto Memorial Hospital, Kyoto 613-0034, Japan
Toshiyuki Kosuga, Department of Surgery, Saiseikai Shiga Hospital, Ritto 520-3046, Japan
Author contributions: Kudou M performed the research, analyzed the data, and wrote the manuscript; Kosuga T made contributions to conception and supervision of the study; Otsuji E critically revised the article; and all authors have read and approved the final manuscript.
Conflict-of-interest statement: The authors declare no conflicts of interests for this article.
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: Toshiyuki Kosuga, MD, PhD, Assistant Professor, Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, 465 Kawaramachi-hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan. toti-k@koto.kpu-m.ac.jp
Received: September 19, 2020
Peer-review started: September 19, 2020
First decision: October 17, 2020
Revised: October 28, 2020
Accepted: November 13, 2020
Article in press: November 13, 2020
Published online: November 28, 2020
Abstract

Artificial intelligence (AI) using machine or deep learning algorithms is attracting increasing attention because of its more accurate image recognition ability and prediction performance than human-aid analyses. The application of AI models to gastrointestinal (GI) clinical oncology has been investigated for the past decade. AI has the capacity to automatically detect and diagnose GI tumors with similar diagnostic accuracy to expert clinicians. AI may also predict malignant potential, such as tumor histology, metastasis, patient survival, resistance to cancer treatments and the molecular biology of tumors, through image analyses of radiological or pathological imaging data using complex deep learning models beyond human cognition. The introduction of AI-assisted diagnostic systems into clinical settings is expected in the near future. However, limitations associated with the evaluation of GI tumors by AI models have yet to be resolved. Recent studies on AI-assisted diagnostic models of gastric and colorectal cancers in the endoscopic, pathological, and radiological fields were herein reviewed. The limitations and future perspectives for the application of AI systems in clinical settings have also been discussed. With the establishment of a multidisciplinary team containing AI experts in each medical institution and prospective studies, AI-assisted medical systems will become a promising tool for GI cancer.

Keywords: Artificial intelligence, Gastric cancer, Colorectal cancer, Endoscopy, Pathology, Radiology

Core Tip: Artificial intelligence (AI) is attracting increasing attention because of its more accurate image recognition ability and prediction performance than human-aid analyses. The application of AI models to gastrointestinal clinical oncology has been investigated, and the findings obtained indicate its capacity for automatic diagnoses with similar accuracy to expert clinicians and the prediction of malignant potential. However, limitations in the evaluation of gastrointestinal tumors by current AI models have yet to be resolved. The limitations of and future perspectives for the application of AI-assisted systems to clinical settings have been discussed herein.