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ArticleInternational urogynecology journal2025

Addressing Commonly Asked Questions in Urogynecology: Accuracy and Limitations of ChatGPT.

Gregory Vurture, Nicole Jenkins, James Ross, Stephanie Sansone, Ellen Conner, Nina Jacobson, Scott Smilen, Jonathan Baum

Abstract read
In one paragraph

Article in International urogynecology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Gregory VurtureDivision of Urogynecology, Department of Urology, Montefiore Medical Center-Albert Einstein College of Medicine, 1250 Waters Place, Tower Two, 9 th Floor, Bronx, NY, 10460, USA. gwv203@nyu.edu.ORCID 0000-0003-3971-7554
Nicole JenkinsDepartment of Obstetrics and Gynecology, Hackensack Meridian Health-Jersey Shore University Medical Center, Neptune City, NJ, USA.
James RossDivision of Urogynecology, Department of Obstetrics and Gynecology, Hackensack Meridian Health-Jersey Shore University Medical Center, Neptune City, NJ, USA.
Stephanie SansoneDivision of Urogynecology, Department of Obstetrics and Gynecology, Hackensack Meridian Health-Jersey Shore University Medical Center, Neptune City, NJ, USA.
Ellen ConnerDivision of Urogynecology, Department of Obstetrics and Gynecology, Hackensack Meridian Health-Jersey Shore University Medical Center, Neptune City, NJ, USA.
Nina JacobsonDivision of Urogynecology, Department of Obstetrics and Gynecology, Hackensack Meridian Health-Jersey Shore University Medical Center, Neptune City, NJ, USA.
Scott SmilenDivision of Urogynecology, Department of Obstetrics and Gynecology, Hackensack Meridian Health-Jersey Shore University Medical Center, Neptune City, NJ, USA.
Jonathan BaumDepartment of Obstetrics and Gynecology, Hackensack Meridian Health-Jersey Shore University Medical Center, Neptune City, NJ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introduction and hypothesisExisting literature suggests that large language models such as Chat Generative Pre-training Transformer (ChatGPT) might provide inaccurate and unreliable health care information. The literature regarding its performance in urogynecology is scarce. The aim of the present study is to assess ChatGPT's ability to accurately answer commonly asked urogynecology patient questions.

methodsAn expert panel of five board certified urogynecologists and two fellows developed ten commonly asked patient questions in a urogynecology office. Questions were phrased using diction and verbiage that a patient may use when asking a question over the internet. ChatGPT responses were evaluated using the Brief DISCERN (BD) tool, a validated scoring system for online health care information. Scores ≥ 16 are consistent with good-quality content. Responses were graded based on their accuracy and consistency with expert opinion and published guidelines.

resultsThe average score across all ten questions was 18.9 ± 2.7. Nine out of ten (90%) questions had a response that was determined to be of good quality (BD ≥ 16). The lowest scoring topic was "Pelvic Organ Prolapse" (mean BD = 14.0 ± 2.0). The highest scoring topic was "Interstitial Cystitis" (mean BD = 22.0 ± 0). ChatGPT provided no references for its responses.

conclusionsChatGPT provided high-quality responses to 90% of the questions based on an expert panel's review with the BD tool. Nonetheless, given the evolving nature of this technology, continued analysis is crucial before ChatGPT can be accepted as accurate and reliable.

Indexed as

GynecologyInternetUrologyFemaleGenerative Artificial IntelligenceHumansSurveys and QuestionnairesArtificial intelligenceLarge language modelMachine learning

Identifiers

PMID40531221
PMCPMC12681482

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LicenceCC BY
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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.