Evidence map›Paper›PMID 41341635›Full record

ReviewActa gastroenterologica Latinoamericana

[The Era of Intelligent Endoscopy: How Artificial Intelligence Empowers Digestive Endoscopy].

Jorge Baquerizo-Burgos, María Egas-Izquierdo, Doménica Cunto, Carlos Robles-Medranda

Abstract readEnglish AbstractReview
In one paragraph

Review in Acta gastroenterologica Latinoamericana. 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

4 authors.

Jorge Baquerizo-BurgosDepartamento de Endoscopia, Instituto Ecuatoriano de Enfermedades Digestivas (IECED), Guayaquil, Ecuador. Departamento de Endoscopia Instituto Ecuatoriano de Enfermedades Digestivas Guayaquil Ecuador.ORCID https://orcid.org/0000-0002-6741-4211
María Egas-IzquierdoDepartamento de Endoscopia, Instituto Ecuatoriano de Enfermedades Digestivas (IECED), Guayaquil, Ecuador. Departamento de Endoscopia Instituto Ecuatoriano de Enfermedades Digestivas Guayaquil Ecuador.ORCID https://orcid.org/0000-0002-3031-0654
Doménica CuntoDepartamento de Endoscopia, Instituto Ecuatoriano de Enfermedades Digestivas (IECED), Guayaquil, Ecuador. Departamento de Endoscopia Instituto Ecuatoriano de Enfermedades Digestivas Guayaquil Ecuador.ORCID https://orcid.org/0000-0003-1337-7096
Carlos Robles-MedrandaDepartamento de Endoscopia, Instituto Ecuatoriano de Enfermedades Digestivas (IECED), Guayaquil, Ecuador. Departamento de Endoscopia Instituto Ecuatoriano de Enfermedades Digestivas Guayaquil Ecuador.ORCID https://orcid.org/0000-0003-2434-3369

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence is a field of science and engineering that focuses on the computational understanding of intelligent behaviors and the creation of artifacts that exhibit such behaviors, enabling computers to function and think similarly to humans. This technology assists in overcoming the various challenges that healthcare professionals face, contributing to the diagnosis, management, and prognosis of patients. Currently, several artificial intelligence models have been developed in the field of digestive endoscopy, including some that enable the detection of anatomical structures that can aid in the training of medical professionals, serve as guides during endoscopic procedures, or assist in stratifying pre-malignant and malignant lesions. This reduces false negatives, providing timely treatments. Computerized lesion detection and diagnostic system exist for different segments of the digestive tract, each with specific functions that provide assistance during procedures. All of this has been done with the aim of reducing risks stemming from human and environmental factors, among others, which can affect early diagnosis and management of diseases. Artificial intelligence models for digestive endoscopy can not only enhance the visual impression for endoscopists but also reduce the learning curve through the application of precise technologies, thus bridging the gap between experienced and less experienced endoscopists. This article discusses the technological advancements of artificial intelligence in digestive endoscopy and related future aspects.

Indexed as

Artificial intelligenceComputer-assisted detectioncomputer-assisted diagnosisdeep learningendoscopy.

Identifiers

PMID41341635
PMCPMC12120952

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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 OpenQuestion graph.