Othman AAA. From algorithm to bedside: Navigating the promise and perils of implementing machine learning for variceal bleeding mortality prediction. World J Hepatol 2026; 18(9): 117720 [DOI: 10.4254/wjh.117720]
Corresponding Author of This Article
Amira A A Othman, MD, PhD, Lecturer, Principal Investigator, Department of Internal Medicine, Suez University, Cairo-Suez Road, Suez 43511, Suez, Egypt. amira.othman@med.suezuni.edu.eg
Research Domain of This Article
Gastroenterology & Hepatology
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editorial
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Othman AAA. From algorithm to bedside: Navigating the promise and perils of implementing machine learning for variceal bleeding mortality prediction. World J Hepatol 2026; 18(9): 117720 [DOI: 10.4254/wjh.117720]
World J Hepatol. Sep 27, 2026; 18(9): 117720 Published online Sep 27, 2026. doi: 10.4254/wjh.117720
From algorithm to bedside: Navigating the promise and perils of implementing machine learning for variceal bleeding mortality prediction
Amira A A Othman
Amira A A Othman, Department of Internal Medicine, Suez University, Suez 43511, Suez, Egypt
Author contributions: Othman AAA conceptualized the editorial theme, reviewed the relevant literature, and wrote the manuscript.
AI contribution statement: Grammarly was used for English grammar only. The writing of this manuscript did not utilize any other artificial intelligence.
Conflict-of-interest statement: The author declares that there are no conflicts of interest related to this work.
Corresponding author: Amira A A Othman, MD, PhD, Lecturer, Principal Investigator, Department of Internal Medicine, Suez University, Cairo-Suez Road, Suez 43511, Suez, Egypt. amira.othman@med.suezuni.edu.eg
Received: December 15, 2025 Revised: February 16, 2026 Accepted: March 25, 2026 Published online: September 27, 2026 Processing time: 277 Days and 6.9 Hours
Abstract
The study by Rech et al recently published in World Journal of Hepatology, represents a significant step forward in the prognostication of acute esophageal variceal bleeding. By developing a rigorously constructed random forest model, validating it in a temporally separated prospective cohort, and deploying it as an online calculator, the authors tackle the crucial “last mile” of clinical machine learning (ML) research. This editorial commends the study’s methodological rigor and its tangible effort to bridge the gap between predictive accuracy and clinical utility. However, we delve into the critical challenges that lie ahead for true integration: The “black box” nature of ML models vs the need for clinician trust and interpretability; the ethical quandary highlighted by race as the top predictive feature; and the practical hurdles of workflow integration, model drift monitoring, and external validation across diverse healthcare settings. The work of Rech et al is not the end of the journey, but a vital prototype that frames the essential next questions for the hepatology community: How do we build trustworthy, equitable, and actionable artificial intelligence tools that genuinely improve outcomes for our highest-risk patients?
Core Tip: Machine learning is transitioning from theoretical promise to practical implementation within hepatology, particularly in high-risk conditions such as acute esophageal variceal bleeding. The study by Rech et al distinguishes itself by combining high-performing mortality prediction with prospective validation and real-world deployment as an online calculator. This editorial highlights why such efforts represent an important step toward bridging the persistent gap between algorithm development and clinical adoption. Yet we also explore the remaining challenges-model interpretability, ethical complexities surrounding race-based predictors, workflow integration, model drift, and the need for multicenter external validation. Understanding these dimensions is crucial for translating artificial intelligence into safer, more equitable, and genuinely impactful tools at the bedside.
