Published online Feb 28, 2022. doi: 10.35712/aig.v3.i1.13
Peer-review started: November 21, 2021
First decision: January 9, 2022
Revised: January 24, 2022
Accepted: February 23, 2022
Article in press: February 23, 2022
Published online: February 28, 2022
Processing time: 95 Days and 17.1 Hours
Artificial Intelligence (AI) is a type of intelligence that comes from machines or computer systems that mimics human cognitive function. Recently, AI has been utilized in medicine and helped clinicians make clinical decisions. In gastroenterology, AI has assisted colon polyp detection, optical biopsy, and diagnosis of Helicobacter pylori infection. AI also has a broad role in the clinical prediction and management of gastrointestinal bleeding. Machine learning can determine the clinical risk of upper and lower gastrointestinal bleeding. AI can assist the management of gastrointestinal bleeding by identifying high-risk patients who might need urgent endoscopic treatment or blood transfusion, determining bleeding stigmata during endoscopy, and predicting recurrence of gastrointestinal bleeding. The present review will discuss the role of AI in the clinical prediction and management of gastrointestinal bleeding, primarily on how it could assist gastroenterologists in their clinical decision-making compared to conventional methods. This review will also discuss challenges in implementing AI in routine practice.
Core Tip: Gastrointestinal bleeding is a common problem in the emergency department. Quick and appropriate clinical decision is needed in the management of gastrointestinal bleeding. Artificial intelligence, namely machine learning and deep learning, can utilize electronic health record data to provide insights which might help clinicians, especially gastroenterologists, in the management of gastrointestinal bleeding. The present review will discuss the roles of artificial intelligence in clinical prediction and management of gastrointestinal bleeding, and compare them to conventional methods. This review will also discuss challenges in the implementation of artificial intelligence in routine practice.