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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
Processing time: 72 Days and 23.9 Hours
Core Tip

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.