Evidence mapPaperPMID 41640959Full record

ReviewWorld journal of hepatology2026

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

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

Abstract readReview
In one paragraph

Review in World journal of hepatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Panagiotis BoutosDepartment of Transplantation Surgery, Center for Research and Innovation in Solid Organ Transplantation, Aristotle University School of Medicine, Thessaloniki 54642, Kentrikí Makedonía, Greece. pgmpoutos@gmail.com.
Konstantina-Eleni KarakasiDepartment of Transplantation Surgery, Center for Research and Innovation in Solid Organ Transplantation, Aristotle University of Thessaloniki, Thessaloniki 54642, Kentrikí Makedonía, Greece.
Georgios KatsanosDepartment of Transplantation Surgery, Center for Research and Innovation in Solid Organ Transplantation, Aristotle University School of Medicine, Thessaloniki 54642, Kentrikí Makedonía, Greece.
Nikolaos AntoniadisDepartment of Transplantation Surgery, Center for Research and Innovation in Solid Organ Transplantation, Aristotle University School of Medicine, Thessaloniki 54642, Kentrikí Makedonía, Greece.
Athanasios KofinasDepartment of Transplantation Surgery, Center for Research and Innovation in Solid Organ Transplantation, Aristotle University of Thessaloniki, Thessaloniki 54642, Kentrikí Makedonía, Greece.
Georgios TsoulfasDepartment of Transplantation Surgery, Center for Research and Innovation in Solid Organ Transplantation, Aristotle University School of Medicine, Thessaloniki 54642, Kentrikí Makedonía, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial intelligenceClinical decision supportDeep learningEndoscopyGastroenterologyHepatologyInterpretabilityMachine learningMedical imagingTrustworthiness

Identifiers

PMID41640959
PMCPMC12865431

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.