Prakash Kumar Sasmal, MS, FNB (Minimal Access Surgery), FACS, FACRSI, Professor, Department of Surgery, All India Institute of Medical Sciences, Sijua, Bhubaneshwar 751019, Odisha, India. surg_prakash@aiimsbhubaneswar.edu.in
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
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/
Artif Intell Gastrointest Endosc. Mar 8, 2026; 7(1): 114426 Published online Mar 8, 2026. doi: 10.37126/aige.v7.i1.114426
Artificial intelligence in upper gastrointestinal bleeding: Can machine learning predict endotherapy requirements?
S Rakesh Kumar, Manas Kumar Panigrahi, Prakash Kumar Sasmal
S Rakesh Kumar, Department of Gastroenterology, Institute of Medical Sciences and SUM Hospital, Bhubaneshwar 751003, Odisha, India
Manas Kumar Panigrahi, Department of Gastroenterology, All India Institute of Medical Sciences, Bhubaneshwar 751019, Odisha, India
Prakash Kumar Sasmal, Department of Surgery, All India Institute of Medical Sciences, Bhubaneshwar 751019, Odisha, India
Co-first authors: S Rakesh Kumar and Manas Kumar Panigrahi.
Author contributions: Kumar SR and Panigrahi MK performed the literature search, collected and analyzed relevant data, and drafted the initial version of the manuscript, and they contributed equally to this manuscript as co-first authors; Panigrahi MK and Sasmal PK contributed to data interpretation, critically reviewed the selected literature, prepared the tables/figures, and participated in revising the manuscript for important intellectual content; Sasmal PK provided overall supervision, guided the interpretation of findings, critically revised the manuscript for significant scholarly content, and agreed to be accountable for all aspects of the work; Kumar SR, Panigrahi MK, and Sasmal PK contributed to the conception and design of the study. All authors have read and approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Prakash Kumar Sasmal, MS, FNB (Minimal Access Surgery), FACS, FACRSI, Professor, Department of Surgery, All India Institute of Medical Sciences, Sijua, Bhubaneshwar 751019, Odisha, India. surg_prakash@aiimsbhubaneswar.edu.in
Received: September 19, 2025 Revised: November 10, 2025 Accepted: December 10, 2025 Published online: March 8, 2026 Processing time: 165 Days and 20.1 Hours
Abstract
Upper gastrointestinal bleeding is a medical emergency requiring prompt triage and management. Although traditionally classic risk scores such as the Rockall score, Glasgow-Blatchford score, and albumin, international normalized ratio, mental status, systolic blood pressure, age > 65 years have been employed to direct initial evaluation, their use in predicting endoscopic intervention remains imperfect. More recent innovations in artificial intelligence and machine learning (ML) have significant potential for clinical decision improvement. This mini-review critically analyses recent advancesin artificial intelligence ML algorithms for managing upper gastrointestinal bleeding, emphasising the need for endoscopic therapy and the prediction of complications. We assessed peer-reviewed literature from 2020 to 2025 and compare ML models to established clinical scores. Although preliminary findings are encouraging, challenges remain regarding generalisation, validation, interpretability, and real-world integration.
Core Tip: There are validated risk assessment scores which are being conventionally used to predict the need for endotherapy and complications in patients with acute upper gastrointestinal bleed (AUGIB). However, those have many limitations which restrict their use effectively and in a timely manner in the real world. In this era of artificial intelligence, these predictive scores can be complemented and/or replaced by machine learning analysis of clinical and laboratory data in conjunction with endoscopy images to diagnose and treat AUGIB patients accurately. In recent years, new evidence has been coming in support of this, and future research is still ongoing. In this mini-review, the use of artificial intelligence in AUGIB is briefly discussed with emphasis on current evidence, its clinical application and future directions.
