Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27(16): 1664-1690 [PMID: 33967550 DOI: 10.3748/wjg.v27.i16.1664]
Corresponding Author of This Article
Xiu-Jun Cai, FACS, FRCS, MD, PhD, Chief Doctor, Professor, Surgeon, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, No. 3 Qingchun East Road, Hangzhou 310016, Zhejiang Province, China. srrsh_cxj@zju.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Jia-Sheng Cao, Ming-Yu Chen, Bin Zhang, Jia-Hao Hu, Shi-Jie Li, Xu Feng, Ji-Liang Shen, Xiu-Jun Cai, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
Zi-Yi Lu, Sarun Juengpanich, Win Topatana, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
Xue-Yin Zhou, School of Medicine, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
Yu Liu, College of Life Sciences, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
Author contributions: Cao JS, Lu ZY, Chen MY, and Cai XJ designed the study and collected the data; Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, and Zhou XY analyzed and interpreted the data; Cao JS, Lu ZY, and Chen MY wrote the manuscript; Cai XJ revised the manuscript; all authors approved the final version of the manuscript.
Supported byZhejiang Medical and Health Science and Technology Project, No. 2019321842; National Natural Science Foundation of China, No. 81827804; and Zhejiang Clinical Research Center of Minimally Invasive Diagnosis and Treatment of Abdominal Diseases, No. 2018E50003.
Conflict-of-interest statement: The authors deny any 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: Xiu-Jun Cai, FACS, FRCS, MD, PhD, Chief Doctor, Professor, Surgeon, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, No. 3 Qingchun East Road, Hangzhou 310016, Zhejiang Province, China. srrsh_cxj@zju.edu.cn
Received: January 15, 2021 Peer-review started: January 15, 2021 First decision: February 9, 2021 Revised: February 11, 2021 Accepted: March 17, 2021 Article in press: March 17, 2021 Published online: April 28, 2021 Processing time: 95 Days and 13 Hours
Abstract
Originally proposed by John McCarthy in 1955, artificial intelligence (AI) has achieved a breakthrough and revolutionized the processing methods of clinical medicine with the increasing workloads of medical records and digital images. Doctors are paying attention to AI technologies for various diseases in the fields of gastroenterology and hepatology. This review will illustrate AI technology procedures for medical image analysis, including data processing, model establishment, and model validation. Furthermore, we will summarize AI applications in endoscopy, radiology, and pathology, such as detecting and evaluating lesions, facilitating treatment, and predicting treatment response and prognosis with excellent model performance. The current challenges for AI in clinical application include potential inherent bias in retrospective studies that requires larger samples for validation, ethics and legal concerns, and the incomprehensibility of the output results. Therefore, doctors and researchers should cooperate to address the current challenges and carry out further investigations to develop more accurate AI tools for improved clinical applications.
Core Tip: Artificial intelligence (AI) technologies are widely used for medical image analysis in the gastroenterology and hepatology fields. Several AI models have been developed for accurate diagnosis, treatment, and prognosis based on images of endoscopy, radiology, pathology, achieving high performance comparable to experts. However, we should be aware of the certain constraints that limit the acceptance and utilization of AI tools in clinical practice. To use AI wisely, doctors and researchers should work together to address the current challenges and develop more accurate AI tools to improve patient care.
