Harnessing artificial intelligence in gastroenterology and hepatology: Current applications and future perspectives

Artificial intelligence (AI) has made remarkable strides, becoming an essential tool in modern medicine. As AI continues to evolve, it is crucial to redefine its scope, classifications, and subtypes to better align with its clinical applications and potential. With a growing number of sophisticated models, AI is now widely used in gastroenterology and hepatology, offering new ways to enhance patient care. In gastroenterology, AI helps doctors identify lesions during endoscopy, detect gastrointestinal bleeding, and support the diagnosis and treatment of conditions like inflammatory bowel disease and gastrointestinal cancers. In hepatology, it aids in staging liver fibrosis, tracking disease progression, and predicting hepatocellular carcinoma risks. Machine learning further personalizes treatment plans, helping physicians make more informed decisions. However, despite its promise, AI still faces hurdles, including biases in data, ethical considerations, regulatory challenges, and the need for better transparency. Moving forward, refining these models, conducting extensive validation studies, and integrating AI seamlessly into clinical practice will be crucial in fully realizing its benefits for gastroenterology and hepatology.

Authors

Institutions

Publication Details

Journal
World Journal of Hepatology
Published
2026-01-27
DOI
https://doi.org/10.4254/wjh.v18.i1.111902
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Harnessing artificial intelligence in gastroenterology and hepatology: Current applications and future perspectives

Athanasios Kofinas, Panagiotis Boutos, Konstantina-Eleni Karakasi, Georgios Katsanos et al.
World Journal of Hepatology
Artificial Intelligence in Healthcare and Education
article

Harnessing artificial intelligence in gastroenterology and hepatology: Current applications and future perspectives

Athanasios Kofinas, Panagiotis Boutos, Konstantina-Eleni Karakasi, Georgios Katsanos, Nikolaos Antoniadis, Georgios Tsoulfas
article en

Abstract

Artificial intelligence (AI) has made remarkable strides, becoming an essential tool in modern medicine. As AI continues to evolve, it is crucial to redefine its scope, classifications, and subtypes to better align with its clinical applications and potential. With a growing number of sophisticated models, AI is now widely used in gastroenterology and hepatology, offering new ways to enhance patient care. In gastroenterology, AI helps doctors identify lesions during endoscopy, detect gastrointestinal bleeding, and support the diagnosis and treatment of conditions like inflammatory bowel disease and gastrointestinal cancers. In hepatology, it aids in staging liver fibrosis, tracking disease progression, and predicting hepatocellular carcinoma risks. Machine learning further personalizes treatment plans, helping physicians make more informed decisions. However, despite its promise, AI still faces hurdles, including biases in data, ethical considerations, regulatory challenges, and the need for better transparency. Moving forward, refining these models, conducting extensive validation studies, and integrating AI seamlessly into clinical practice will be crucial in fully realizing its benefits for gastroenterology and hepatology.

World Journal of HepatologyVol. 18(1)
Aristotle University of Thessaloniki (GR)
Openalex Percentile: Top 80%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.