Evidence map›Paper›PMID 38481787›Full record

ReviewAnnals of gastroenterology

Artificial intelligence in gastrointestinal endoscopy: a comprehensive review.

Hassam Ali, Muhammad Ali Muzammil, Dushyant Singh Dahiya, Farishta Ali, Shafay Yasin, Waqar Hanif, Manesh Kumar Gangwani, Muhammad Aziz, Muhammad Khalaf, Debargha Basuli and 1 more

Open access · diamondAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Annals of gastroenterology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 3 pooled it
11.0field-weighted citation impact, top 1% of its field
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

29 citing papers in PubMed, 3 syntheses or guidelines pooled it, 39 citations in OpenAlex.

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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

11 authors at 1 institution in 1 country.

Hassam AliDepartment of Gastroenterology and Hepatology, ECU Health Medical Center/Brody School of Medicine, Greenville, North Carolina, USA (Hassam Ali, Muhammad Khalaf).
Muhammad Ali MuzammilDepartment of Internal Medicine, Dow University of Health Sciences, Sindh, PK (Muhammad Ali Muzammil).
Dushyant Singh DahiyaDivision of Gastroenterology, Hepatology & Motility, The University of Kansas School of Medicine, Kansas City, Kansas, USA (Dushyant Singh Dahiya).
Farishta AliDepartment of Internal Medicine, Khyber Girls Medical College, Peshawar, PK (Farishta Ali).
Shafay YasinDepartment of Internal Medicine, Quaid-e-Azam Medical College, Punjab, PK (Shafay Yasin, Waqar Hanif).
Waqar HanifDepartment of Internal Medicine, Quaid-e-Azam Medical College, Punjab, PK (Shafay Yasin, Waqar Hanif).
Manesh Kumar GangwaniDepartment of Medicine, University of Toledo Medical Center, Toledo, OH, USA (Manesh Kumar Gangwani).
Muhammad AzizDepartment of Gastroenterology and Hepatology, The University of Toledo Medical Center, Toledo, OH, USA (Muhammad Aziz).
Muhammad KhalafDepartment of Gastroenterology and Hepatology, ECU Health Medical Center/Brody School of Medicine, Greenville, North Carolina, USA (Hassam Ali, Muhammad Khalaf).
Debargha BasuliDepartment of Internal Medicine, East Carolina University/Brody School of Medicine, Greenville, North Carolina, USA (Debargha Basuli).
Mohammad Al-HaddadDivision of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN, USA (Mohammad Al-Haddad).
Vidant Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integrating artificial intelligence (AI) into gastrointestinal (GI) endoscopy heralds a significant leap forward in managing GI disorders. AI-enabled applications, such as computer-aided detection and computer-aided diagnosis, have significantly advanced GI endoscopy, improving early detection, diagnosis and personalized treatment planning. AI algorithms have shown promise in the analysis of endoscopic data, critical in conditions with traditionally low diagnostic sensitivity, such as indeterminate biliary strictures and pancreatic cancer. Convolutional neural networks can markedly improve the diagnostic process when integrated with cholangioscopy or endoscopic ultrasound, especially in the detection of malignant biliary strictures and cholangiocarcinoma. AI's capacity to analyze complex image data and offer real-time feedback can streamline endoscopic procedures, reduce the need for invasive biopsies, and decrease associated adverse events. However, the clinical implementation of AI faces challenges, including data quality issues and the risk of overfitting, underscoring the need for further research and validation. As the technology matures, AI is poised to become an indispensable tool in the gastroenterologist's arsenal, necessitating the integration of robust, validated AI applications into routine clinical practice. Despite remarkable advances, challenges such as operator-dependent accuracy and the need for intricate examinations persist. This review delves into the transformative role of AI in enhancing endoscopic diagnostic accuracy, particularly highlighting its utility in the early detection and personalized treatment of GI diseases.

Indexed as

artificial intelligencedeep learninggastroenterologymachine learningMedical imaging

Identifiers

PMID38481787
PMCPMC10927620
OpenAlexW4392007149

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.