Artificial intelligence in upper gastrointestinal bleeding: Can machine learning predict endotherapy requirements?

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.