Evidence map›Paper›PMID 40539200›Full record

ArticleWorld journal of gastroenterology2025

Is the use of artificial intelligence the main stage for detecting polyps during colonoscopy?

Vladislav V Tsukanov, Alexander V Vasyutin, Edward V Kasparov, Julia L Tonkikh

Abstract readLetter
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Vladislav V TsukanovClinical Department of the Digestive System Pathology of Adults and Children, Federal Research Center "Krasnoyarsk Science Center" of the Siberian Branch of the Russian Academy of Sciences, Scientific Research Institute of Medical Problems of the North, Krasnoyarsk 660022, Russia.
Alexander V VasyutinClinical Department of the Digestive System Pathology of Adults and Children, Federal Research Center "Krasnoyarsk Science Center" of the Siberian Branch of the Russian Academy of Sciences, Scientific Research Institute of Medical Problems of the North, Krasnoyarsk 660022, Russia.
Edward V KasparovClinical Department of the Digestive System Pathology of Adults and Children, Federal Research Center "Krasnoyarsk Science Center" of the Siberian Branch of the Russian Academy of Sciences, Scientific Research Institute of Medical Problems of the North, Krasnoyarsk 660022, Russia.
Julia L TonkikhClinical Department of the Digestive System Pathology of Adults and Children, Federal Research Center "Krasnoyarsk Science Center" of the Siberian Branch of the Russian Academy of Sciences, Scientific Research Institute of Medical Problems of the North, Krasnoyarsk 660022, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer (CRC) is the third most frequently diagnosed cancer and the second leading cause of cancer death worldwide. In this regard, CRC screening is one of the most important issues in modern preventive medicine. Colorectal polyps are potential predictors of CRC, and therefore represent one of the leading targets for screening colonoscopy. The difficulty of analyzing the information obtained during colonoscopy, including the size, location, shape, type of polyps, the need to standardize morphological data, determines that recently a number of works have promoted the opinion on the advisability of using various artificial intelligence (AI) methods to improve the effectiveness of endoscopic screening for CRC. At the same time, they point to a number of errors and methodological problems in the use of AI systems for the diagnosis of colorectal polyps. In this regard, the interpretation of the work of Shi

Indexed as

Artificial IntelligenceColonic PolypsColonoscopyColorectal NeoplasmsEarly Detection of CancerColonHumansMachine LearningMass ScreeningArtificial intelligenceColonoscopyColorectal cancerColorectal polypsDiagnostics

Identifiers

PMID40539200
PMCPMC12175847

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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.