Evidence map›Paper›PMID 40917764›Full record

ReviewGastro hep advances2025

Provider Attitudes and Perceptions on Using Artificial Intelligence in Colonoscopy: A Systematic Review and Meta-Analysis.

Saeed Soleymanjahi, Niroop Rajashekar, Sunny Chung, Alyssa A Grimshaw, Mary Jo K Tvedt, Farid Foroutan, Shahnaz Sultan, Dennis L Shung, Jennifer M Kolb

Abstract readReview
In one paragraph

Review in Gastro hep advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Saeed SoleymanjahiDivision of Gastroenterology, Mass General Brigham, Harvard School of Medicine, Boston, Massachusetts.
Niroop RajashekarDepartment of Medicine, Yale School of Medicine, New Haven, Connecticut.
Sunny ChungSection of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, Connecticut.
Alyssa A GrimshawCushing/Whitney Medical Library, Yale University, New Haven, Connecticut.
Mary Jo K TvedtSchool of Science, Suffolk University & Mass General Brigham, Harvard School of Medicine, Boston, Massachusetts.
Farid ForoutanTed Rogers Centre for Heart Research, University Health Network, Toronto, Canada.
Shahnaz SultanDivision of Gastroenterology, Hepatology and Nutrition, University of Minnesota, Minneapolis, Minnesota.
Dennis L ShungSection of Digestive Diseases, Clinical and Translational Research Accelerator, Department of Biomedical Informatics and Data Science, Department of Medicine, Yale School of Medicine, New Haven, Connecticut.
Jennifer M KolbVatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at UCLA, Los Angeles, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: Colonoscopy is the gold standard screening modality for colorectal cancer; however, it is operator-dependent and reliant on exam quality. Incorporating artificial intelligence (AI) into colonoscopy may improve adenoma detection and clinical outcomes, but this is a sociotechnical challenge that requires effective human-AI teaming incorporating provider attitudes. Methods: We conducted a systematic review of studies evaluating attitudes and perspectives of providers toward AI-assisted colonoscopy. Participant responses to outcome questions of interest were combined across the studies to calculate pooled proportion (Pp) and 95% confidence interval (CI). Top-ranked perceived advantages and disadvantages in each study were defined as the items that >50% of the study participants voted for. Results: Out of 2044 abstracts screened, 13 studies were included representing 1538 providers who were mostly gastroenterologists or trainees and 25%-100% had direct experience using AI in a clinical setting. Overall, a large majority were interested in using AI (Pp = 80%, 95% CI 70%-89%, n = 8 studies) and believed it can improve adenoma or polyp detection rate (Pp = 74%, 95% CI 68%-80%, n = 4 studies). Among 5 studies addressing financial implications, about half were concerned about the cost of using AI (52%, 95% CI 24%-79%). An average of 38% of respondents (95% CI 9%-73%) from 4 studies raised concern regarding accountability for misdiagnosis. High number of false positives and an absence of clinical guidelines were top-ranked perceived disadvantages in 2 studies. Conclusion: Most gastroenterology providers expressed interest in using AI systems with colonoscopy and believed it can improve adenoma detection rate. Cost, high number of false positives, and lack of professional society guidelines were among top perceived concerns.

Indexed as

Adenoma detection rateArtificial intelligenceColonoscopy

Identifiers

PMID40917764
PMCPMC12410009

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.