From prediction to clinical decision-making: Explainable machine learning in acute suppurative cholecystitis

doi:10.3748/wjg.119370

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastroenterol. Sep 28, 2026; 32(36): 119370
Published online Sep 28, 2026. doi: 10.3748/wjg.119370

From prediction to clinical decision-making: Explainable machine learning in acute suppurative cholecystitis

Maria Kapritsou

Maria Kapritsou, Nursing Directorate, Hellenic Anticancer Institute, Saint Savvas Hospital, Athens 11522, Greece

Author contributions: Kapritsou M conceptualized the manuscript, conducted the literature review, and wrote and approved the final version.

AI contribution statement: The manuscript was not AI-generated. AI tools were not used to generate the scientific content of the manuscript. No part of the data analysis was performed using AI. No images in the manuscript were generated by AI. The references were not generated by AI.

Conflict-of-interest statement: The author declares no conflicts of interest.

Corresponding author: Maria Kapritsou, PhD, Deputy Director, Postdoc, Nursing Directorate, Hellenic Anticancer Institute, Saint Savvas Hospital, Av Alexandras 171, Athens 11522, Greece. mariakaprit@gmail.com

Received: January 26, 2026
Revised: February 23, 2026
Accepted: March 10, 2026
Published online: September 28, 2026
Processing time: 208 Days and 4.9 Hours

Core Tip

Core Tip: Early and accurate identification of acute suppurative cholecystitis is essential for timely surgical intervention and improved outcomes. Explainable machine learning models that integrate clinical and radiomic data provide transparent risk stratification, supporting surgical decision-making and perioperative prioritization. When embedded into multidisciplinary care pathways, such tools may enhance patient safety and real-world management of acute biliary disease.