Evidence map›Paper›PMID 39866721›Full record

ArticleGastro hep advances2025

Can Artificial Intelligence Create an Accurate Colonoscopy Bowel Preparation Prompt?

Marni H Wilkoff, Nicholas R Piniella, Rashmi Advani

Abstract read
In one paragraph

Article 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

3 authors.

Marni H WilkoffDepartment of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Nicholas R PiniellaNew York Institute of Technology College of Osteopathic Medicine, Glen Head, New York.
Rashmi AdvaniDivision of Gastroenterology, Icahn School of Medicine at Mount Sinai, Mount Sinai South Nassau, One Healthy Way, Oceanside, New York.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: Colorectal cancer is the third most common cancer in the United States, with colonoscopy being the preferred screening method. Up to 25% of colonoscopies are associated with poor preparation which leads to prolonged procedure time, repeat colonoscopies, and decreased adenoma detection. Artificial intelligence (AI) is being increasingly used in medicine, assessing medical school exam questions, and writing medical reports. Its use in gastroenterology has been used to educate patients with cirrhosis and hepatocellular carcinoma, answer patient questions about colonoscopy and provide correct colonoscopy screening intervals, having the ability to augment the patient-provider relationship. This study aims at assessing the accuracy of a ChatGPT-generated precolonoscopy bowel preparation prompt. Methods: A nonrandomized cross-sectional study assessing the perceptions of an AI-generated colonoscopy preparation prompt was conducted in a large multisite quaternary health-care institution in the northeast United States. All practicing gastroenterologists in the health system were surveyed, with 208 having a valid email address and were included in the study. A Research Electronic Data Capture survey was then distributed to all participants and analyzed using descriptive statistics. Results: Overall, 91% of gastroenterologist physicians determined the prompt easy to understand, 95% thought the prompt was scientifically accurate and 66% were comfortable giving the prompt to their patients. Sixty four percent of reviewers correctly identified the ChatGPT-generated prompt, but only 32% were confident in their answer. Conclusion: The ability of ChatGPT to create a sufficient bowel preparation prompt highlights how physicians can incorporate AI into clinical practice to improve ease and efficiency of communication with patients when it comes to bowel preparation.

Indexed as

Adenoma Detection RateColon Cancer ScreeningColonoscopy PreparationMachine LearningSplit-Dose Bowel Preparation

Identifiers

PMID39866721
PMCPMC11758412

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.