TY  - JOUR
T1  - Artificial Intelligence-driven Phenomics in Celiac Disease: Toward Precision Diagnosis 
AU  - Rahmoune, Hakim
AU  - Boutrid, Nada
AU  - Benchoufi, Isra
JF  - Journal of Translational Gastroenterology
VL  - 
IS  - 000
SN  - 2994-8754
SP  - 
EP  - 
Y1  - 2026-09-30
DO  - 10.14218/JTG.2026.00011
UR  - https://www.xiahepublishing.com/2994-8754/JTG-2026-00011
AB  - Celiac disease remains underdiagnosed despite an established diagnostic pathway, reflecting phenotypic heterogeneity, delayed recognition, and dependence on resource-intensive testing. This narrative review synthesizes evidence on artificial intelligence and phenomics in celiac disease (CD) across four operational layers: electronic health record phenotyping, Human Phenotype Ontology-based semantic encoding, machine-learning pre-screening from routine clinical data, and deep-learning-assisted histopathology. A targeted literature search of PubMed/MEDLINE, Embase, and Google Scholar covered publications from January 2010 through December 2025, using combinations of CD/coeliac disease with artificial intelligence, machine learning, deep learning, phenomics, Human Phenotype Ontology, computable phenotype, electronic health records, and natural language processing, supplemented by targeted searches and citation chaining. We present a curated minimum viable CD phenome and discuss clinical actionability, age-specific considerations, and current pediatric validation gaps. Selected models have demonstrated promising CD detection or pre-screening performance in specific datasets, but prospective, multicenter evidence with external validation remains insufficient to establish clinical utility. Artificial intelligence should augment expert clinical care rather than replace it.