Introduction for the Artificial Intelligence and Gastrointestinal Cancer Column
Artificial Intelligence and Gastrointestinal Cancer Column

Introduction for the Artificial Intelligence and Gastrointestinal Cancer Column

Brandon J. Teng, MD

Division of Gastroenterology, University of Washington, Seattle, WA, USA. (Email:

Michael F. Byrne, MD

Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada. (Email:

Received: 16 June 2019; Accepted: 29 June 2019; Published: 09 July 2019.

doi: 10.21037/jmai.2019.06.03

Gastrointestinal (GI) cancer is a leading cause worldwide of morbidity and mortality. In 2018, GI cancer accounted for 27% of all new cancer diagnoses. The incidence rate of colorectal cancer is rising in many countries, with a recent dramatic increase for people under the age of 50 years. Additionally, esophageal, stomach, and pancreatic cancer are often diagnosed at late stages leading to high rates of morbidity and mortality. Advances in artificial intelligence (AI) with machine learning and deep learning are rapidly evolving, and will soon change how gastroenterologists screen for, detect, and treat GI cancer. Currently, screening and surveillance of GI cancer consists of, for example, high definition white light endoscopy, endoscopic ultrasound, stool based tests or radiologic imaging. Challenges with these methods include missing small lesions, determining the likelihood of dysplasia of polyps, or predicting depth of invasion prior to lesion resection. Techniques such as narrow band imaging or dye staining are used to assist in optical evaluation of lesions, although these methods have their pitfalls and are time consuming. Optimal clinical outcomes are also influenced by the expertise of the endoscopist, and even in the best of circumstances physicians are subject to human error and fatigue. With advances in AI, innovative applications and improving technology aim to reduce and eliminate many of these factors to improve clinical outcomes. This column will focus on novel developments within the AI and gastroenterology space with a specific focus on improving methods for detection, surveillance, and treatment of GI cancer.

Brandon J. Teng
Michael F. Byrne


Funding: None.


Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Medical Artificial Intelligence for the series “Artificial Intelligence and Gastrointestinal Cancer Column”. The article did not undergo external peer review.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at The series “Artificial Intelligence and Gastrointestinal Cancer Column” was commissioned by the editorial office without any funding or sponsorship. BJT served as the unpaid Guest Editor of the series. MB served as the unpaid Guest Editor of the series and serves as an unpaid editorial board member of Journal of Medical Artificial Intelligence. Michael Byrne is the CEO and Founder, Satisfai Health. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See:

doi: 10.21037/jmai.2019.06.03
Cite this article as: Teng BJ, Byrne MF. Introduction for the Artificial Intelligence and Gastrointestinal Cancer Column. J Med Artif Intell 2019;2:14.

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