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World J Gastroenterol. Jun 7, 2021; 27(21): 2818-2833
Published online Jun 7, 2021. doi: 10.3748/wjg.v27.i21.2818
Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology
Hiroshi Yoshida, Tomoharu Kiyuna
Hiroshi Yoshida, Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo 104-0045, Japan
Tomoharu Kiyuna, Digital Healthcare Business Development Office, NEC Corporation, Tokyo 108-8556, Japan
Author contributions: Yoshida H and Kiyuna T contributed equally to this work.
Conflict-of-interest statement: All authors have no competing interests to be declared.
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: Hiroshi Yoshida, MD, PhD, Staff Physician, Department of Diagnostic Pathology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan. hiroyosh@ncc.go.jp
Received: February 4, 2021
Peer-review started: February 4, 2021
First decision: March 6, 2021
Revised: March 16, 2021
Accepted: April 28, 2021
Article in press: April 28, 2021
Published online: June 7, 2021
Processing time: 111 Days and 22.5 Hours
Abstract

Tremendous advances in artificial intelligence (AI) in medical image analysis have been achieved in recent years. The integration of AI is expected to cause a revolution in various areas of medicine, including gastrointestinal (GI) pathology. Currently, deep learning algorithms have shown promising benefits in areas of diagnostic histopathology, such as tumor identification, classification, prognosis prediction, and biomarker/genetic alteration prediction. While AI cannot substitute pathologists, carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology practice. Regardless of these promising advances, unlike the areas of radiology or cardiology imaging, no histopathology-based AI application has been approved by a regulatory authority or for public reimbursement. Thus, implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology practice. The challenges have been identified at different stages of the development process, such as needs identification, data curation, model development, validation, regulation, modification of daily workflow, and cost-effectiveness balance. The aim of this review is to present challenges in the process of AI development, validation, and regulation that should be overcome for its implementation in real-life GI pathology practice.

Keywords: Artificial intelligence; Deep learning; Digital image analysis; Digital pathology; Clinical implementation; Gastrointestinal cancer

Core Tip: The advances in artificial intelligence (AI) will revolutionize medical practice, as well as other areas of medicine. Deep learning algorithms have shown promising benefits in various areas of diagnostic histopathology. Despite this, AI technology is not widely used as a medical device and is not approved by a regulatory authority. Thus, implying that certain improvements in the development process are still necessary for the implementation of AI in the real-life histopathology-practice. This paper aims to provide a review of recent AI developments in gastrointestinal pathology and the challenges in their implementation.