The utility of artificial intelligence in visualization of pediatric gastrointestinal mucosa

The utilization of artificial intelligence (AI) is rapidly expanding in all areas of medicine. Pediatric gastroenterology is among the fields exploring the use of AI to better visualize the gastrointestinal tract and improve diagnosis, disease subtyping, lesion detection, risk prediction, and treatment optimization for better patient outcomes. AI shows promising developments and applications in complex diseases, such as Crohn's disease, polyposis syndromes, and eosinophilic esophagitis, where diagnosis and initial or subsequent management are impacted by mucosal visualization and analysis. This article summarizes how AI, machine learning, and these complex networks work in addition to addressing the limitations and ethical challenges faced with use of this budding technology. Although most available information on this topic comes from adult literature, this discussion focuses on current and emerging pediatric research and applications of AI in pediatric diagnostic and interventional endoscopy.

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Publication Details

Journal
Frontiers in Pediatrics
Published
2026-01-12
DOI
https://doi.org/10.3389/fped.2025.1739000
Primary Topic
Gastrointestinal Bleeding Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
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article

The utility of artificial intelligence in visualization of pediatric gastrointestinal mucosa

Jeremy Stewart, Bradley Barth, Isabel Rojas
Frontiers in Pediatrics
Gastrointestinal Bleeding Diagnosis and Treatment
article

The utility of artificial intelligence in visualization of pediatric gastrointestinal mucosa

Jeremy Stewart, Bradley Barth, Isabel Rojas
article en

Abstract

The utilization of artificial intelligence (AI) is rapidly expanding in all areas of medicine. Pediatric gastroenterology is among the fields exploring the use of AI to better visualize the gastrointestinal tract and improve diagnosis, disease subtyping, lesion detection, risk prediction, and treatment optimization for better patient outcomes. AI shows promising developments and applications in complex diseases, such as Crohn's disease, polyposis syndromes, and eosinophilic esophagitis, where diagnosis and initial or subsequent management are impacted by mucosal visualization and analysis. This article summarizes how AI, machine learning, and these complex networks work in addition to addressing the limitations and ethical challenges faced with use of this budding technology. Although most available information on this topic comes from adult literature, this discussion focuses on current and emerging pediatric research and applications of AI in pediatric diagnostic and interventional endoscopy.

Frontiers in PediatricsVol. 13
The University of Texas Southwestern Medical Center (US)
Quality Education
Openalex Percentile: Top 91%
Gastrointestinal Bleeding Diagnosis and Treatment
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