Amante MF. Artificial intelligence in histopathology of inflammatory bowel disease: Toward objective and reproducible assessment of disease activity. Artif Intell Gastroenterol 2026; 7(2): 118230 [DOI: 10.35712/aig.v7.i2.118230]
Corresponding Author of This Article
Marcelo Fabián Amante, MD, Chief, División Patología, Hospital General de Agudos Cosme Argerich, Pi y Margall 480, C1155AHA Ciudad Autónoma de Buenos Aires, Argentina. marcelofabianamante@gmail.com
Research Domain of This Article
Pathology
Article-Type of This Article
review-article
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Artif Intell Gastroenterol. Aug 8, 2026; 7(2): 118230 Published online Aug 8, 2026. doi: 10.35712/aig.v7.i2.118230
Artificial intelligence in histopathology of inflammatory bowel disease: Toward objective and reproducible assessment of disease activity
Marcelo Fabián Amante
Marcelo Fabián Amante, División Patología, Hospital General de Agudos Cosme Argerich, C1155AHA Ciudad Autónoma de Buenos Aires, Argentina
Author contributions: Amante MF exclusively carried out the conceptualization and design of the minireview, the creation of all figures and illustrations, the overall supervision of the process, and the critical revisions of all manuscript versions; he also performed the literature search, the analysis and interpretation of the relevant evidence, and the complete drafting of the original manuscript.
AI contribution statement: I hereby clarify that AI was used to improve the grammar, polish the language, and assist with the translation, but not to generate the ideas, texts, or problems presented in the text, nor the solutions or philosophical tensions it proposes.
Conflict-of-interest statement: The author declares no conflicts of interest.
Corresponding author: Marcelo Fabián Amante, MD, Chief, División Patología, Hospital General de Agudos Cosme Argerich, Pi y Margall 480, C1155AHA Ciudad Autónoma de Buenos Aires, Argentina. marcelofabianamante@gmail.com
Received: December 28, 2025 Revised: January 18, 2026 Accepted: March 2, 2026 Published online: August 8, 2026 Processing time: 222 Days and 15.5 Hours
Abstract
The integration of artificial intelligence (AI) into the histopathological assessment of inflammatory bowel disease (IBD) represents a fundamental paradigm shift toward a new era of computational pathology, promising unprecedented levels of objectivity, reproducibility, and clinical insight. Traditional semiquantitative histological scoring systems while clinically entrenched are intrinsically limited by significant inter-observer variability and subjective interpretation, complicating critical decisions in diagnosis, therapeutic monitoring, and the evaluation of histological remission, an increasingly paramount treatment goal. The advent of whole-slide imaging and sophisticated AI algorithms, particularly deep convolutional neural networks and vision transformers, offers a transformative solution. These technologies enable the automated, pixel-level analysis of mucosal architecture and inflammatory infiltrates, translating visual patterns into quantitative, continuous data. This comprehensive minireview systematically synthesizes the current state of AI applications in IBD histopathology, spanning automated crypt analysis, inflammatory cell quantification, and the prediction of established histological scores and clinical outcomes. It critically examines the potential of AI to operationalize and standardize the endpoint of histological remission, thereby strengthening clinical trials and precision medicine. Furthermore, the manuscript delves into the profound epistemological and practical challenges posed by this technological integration, including algorithmic bias, the “black box” dilemma, and the evolving role of the pathologist as an overseer of AI systems. Finally, it outlines a strategic roadmap for future translation, emphasizing the need for robust multicenter validation, standardized regulatory frameworks, seamless clinical workflow integration, and the cultivation of interdisciplinary expertise to fully realize the potential of AI in optimizing personalized, predictive care for patients with IBD.
Core Tip: Artificial intelligence is revolutionizing inflammatory bowel disease histopathology by using deep learning on whole-slide images to automate analysis. This addresses the subjectivity of current scoring systems, offering objective, quantitative data for diagnosis, monitoring, and defining histological remission. While promising for clinical trials and precision medicine, challenges like algorithmic bias and integration into pathology workflows must be overcome through validation, regulation, and interdisciplinary collaboration.
