Fouad Y, Mostafa AM, Abdelhalim SM, Eslam M. Letter to the Editor: Artificial intelligence in hepatology - when deep learning meets drug-induced liver injury. World J Gastroenterol 2026; 32(35): 119939 [DOI: 10.3748/wjg.119939]
Corresponding Author of This Article
Yasser Fouad, MD, Professor, Department of Endemic Medicine and Gastroenterology, Faculty of Medicine, Minia University, Main Road, Minia 19111, Egypt. yasserfouad10@yahoo.com
Research Domain of This Article
Gastroenterology & Hepatology
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letter
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Fouad Y, Mostafa AM, Abdelhalim SM, Eslam M. Letter to the Editor: Artificial intelligence in hepatology - when deep learning meets drug-induced liver injury. World J Gastroenterol 2026; 32(35): 119939 [DOI: 10.3748/wjg.119939]
World J Gastroenterol. Sep 21, 2026; 32(35): 119939 Published online Sep 21, 2026. doi: 10.3748/wjg.119939
Letter to the Editor: Artificial intelligence in hepatology - when deep learning meets drug-induced liver injury
Yasser Fouad, Alaa M Mostafa, Safaa M Abdelhalim, Mohammed Eslam
Yasser Fouad, Alaa M Mostafa, Safaa M Abdelhalim, Department of Endemic Medicine and Gastroenterology, Faculty of Medicine, Minia University, Minia 19111, Egypt
Mohammed Eslam, Storr Liver Centre, Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Sydney 2145, Australia
Author contributions: Fouad Y and Mostafa AM designed the plan of writing; Fouad Y, Mostafa AM, Abdelhalim SM, and Eslam M participated in collection of data, writing and reviewing the manuscript.
AI contribution statement: I used paraphrasing tool to check and correct my language throughout the introduction and other parts (Quillbot).
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
Corresponding author: Yasser Fouad, MD, Professor, Department of Endemic Medicine and Gastroenterology, Faculty of Medicine, Minia University, Main Road, Minia 19111, Egypt. yasserfouad10@yahoo.com
Received: February 11, 2026 Revised: March 21, 2026 Accepted: May 27, 2026 Published online: September 21, 2026 Processing time: 188 Days and 22 Hours
Core Tip
Core Tip: Drug-induced liver injury remains challenging due to its vague presentation and reliance on exclusion-based techniques. This complexity is particularly evident in cases of pyrrolizidine alkaloid-induced hepatic sinusoidal obstruction syndrome, which often require invasive treatments and expert imaging interpretation. Recent advancements in deep learning applied to computed tomography are changing the diagnostic landscape by identifying subtle, diffuse parenchymal abnormalities that might be missed using standard methods. Artificial intelligence can improve clinician performance, enhance diagnostic consistency, and reduce interpretation times, as demonstrated by the validated model discussed here. These integrated technologies could improve the early identification of complex drug-induced liver injury characteristics.
