Artificial Intelligence in Gastrointestinal Disease: Diagnosis and Management in 2025

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 938

Special Issue Editors

Special Issue Information

Dear Colleagues,

Gastrointestinal disease (GID), the disease of the gastrointestinal tract, is one of the main contributors to disease burden around the world. GID causes 8 million deaths around the world per year and cost USD 120 billion in the United States in 2018 alone. Simultaneously, the notion of artificial intelligence (AI) has gained great attention on a global level. This Special Issue is expected to demonstrate the effectiveness and popularity of AI as a cutting-edge approach to the diagnosis and management of GID. This Special Issue is designed to cover a wide range of important topics including the classification, detection, and segmentation of gastrointestinal cancer and inflammatory bowel disease. The Special Issue is intended to address the utility of endoscopic AI, explainable AI, and large language models as well.

Dr. Eun-Sun Kim
Dr. Kwang-Sig Lee
Guest Editors

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Keywords

  • gastrointestinal disease
  • artificial intelligence
  • gastrointestinal cancer
  • inflammatory bowel disease
  • endoscopic AI
  • large language models

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14 pages, 739 KB  
Systematic Review
Genetic Artificial Intelligence in Gastrointestinal Disease: A Systematic Review
by Kwang-Sig Lee and Eun Sun Kim
Diagnostics 2025, 15(17), 2227; https://doi.org/10.3390/diagnostics15172227 - 2 Sep 2025
Viewed by 508
Abstract
Background/Objectives: The application of predictive and explainable artificial intelligence to bioinformatics data such as single nucleotide polymorphism (SNP) information is attracting rising attention in the diagnosis of various diseases. However, there are few reviews available on the recent progress of genetic artificial [...] Read more.
Background/Objectives: The application of predictive and explainable artificial intelligence to bioinformatics data such as single nucleotide polymorphism (SNP) information is attracting rising attention in the diagnosis of various diseases. However, there are few reviews available on the recent progress of genetic artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The purpose of this study is to complete a systematic review on the recent progress of genetic artificial intelligence in GID. Methods: The source of data was ten original studies from PubMed. The ten original studies were eligible according to the following criteria: (participants) the dependent variable of GID or associated disease; (interventions/comparisons) artificial intelligence; (outcomes) accuracy, the area under the curve (AUC), and/or variable importance; a publication year of 2010 or later; and the publication language of English. Results: The performance outcomes reported varied within 79–100 for accuracy (%) and 63–98 for the AUC (%). Random forest was the best approach (AUC 98%) for the classification of inflammatory bowel disease with 13 single nucleotide polymorphisms (SNPs). Similarly, random forest was the best method (R-square 99%) for the regression of the gut microbiome SNP saturation number. The following SNPs were discovered to be major variables for the prediction of GID or associated disease: rs2295778, rs13337626, rs2296188, rs2114039 (esophageal adenocarcinoma); rs28785174, rs60532570, rs13056955, rs7660164 (Crohn’s disease early intestinal resection); rs4945943 (Crohn’s disease); rs316115020, rs316420452 (calcium metabolism); rs738409_G, rs2642438_A, rs58542926_T, rs72613567_TA (steatotic liver disease); rs148710154, rs75146099 (esophageal squamous cell carcinoma). The following demographic and health-related variables were found to be important predictors of GID or associated disease besides SNPs: age, body mass index, disease behavior, immune cell type, intestinal microbiome, MARCKS protein, smoking, and SNP density/number. No deep learning study was found even though deep learning was used as a search term together with machine learning. Conclusions: Genetic artificial intelligence is effective and non-invasive as a decision support system for GID. Full article
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