Evidence map›Paper›PMID 41128396›Full record

ArticleActa obstetricia et gynecologica Scandinavica2026

ChatGPT in urogynecology: Comparing large language model responses to human experts.

Reut Rotem, Craven Simon, Misgav Rottenstreich, Barry A O'reilly, Adi Y Weintraub, Orfhlaith E O'Sullivan

Abstract readComparative Study
In one paragraph

Article in Acta obstetricia et gynecologica Scandinavica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Patient education materials by ChatGPT: Real intention and interpretation.Acta obstetricia et gynecologica Scandinavica · 2026
    Article
  2. Aligning AI and clinical expertise: A collaborative path for patient education.Acta obstetricia et gynecologica Scandinavica · 2026
    Article
  3. Article
  4. Article
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

6 authors.

Reut RotemDepartment of Urogynaecology, Cork University Maternity Hospital, Cork, Ireland.ORCID https://orcid.org/0000-0002-4063-5911
Craven SimonDepartment of Urogynecology, National Maternity Hospital, Dublin 2, Ireland.
Misgav RottenstreichDepartment of Obstetrics and Gynecology, Shaare Zedek Medical Center, Affiliated with the Hebrew University School of Medicine, Jerusalem, Israel.ORCID https://orcid.org/0000-0003-4875-1455
Barry A O'reillyDepartment of Urogynaecology, Cork University Maternity Hospital, Cork, Ireland.ORCID https://orcid.org/0000-0003-4031-9414
Adi Y WeintraubDepartment of Obstetrics and Gynecology, Faculty of Health Sciences, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Orfhlaith E O'SullivanDepartment of Urogynaecology, Cork University Maternity Hospital, Cork, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionLarge language models (LLMs) are increasingly used in healthcare, including urogynecology, where stigma may limit open discussion. LLM-based chat platforms may provide a less intimidating and more accessible way for patients to obtain information, but their reliability requires evaluation. This study compared the quality of ChatGPT-generated responses in urogynecology with those provided by a consultant urogynecologist, focusing on understandability, helpfulness, and reassurance. MATERIAL AND

methodsA cross-sectional survey was conducted among urogynecology patients. After informed consent, participants reviewed responses to six common questions, each answered by ChatGPT and a single consultant. A blinded third-party consultant verified clinical accuracy. Patients rated responses using a 5-point Likert scale across three domains (maximum score 15 per response). Wilcoxon signed-rank tests were used for comparison.

resultsA total of 203 patients participated (median age 56 years, interquartile range 46-66). ChatGPT responses received higher total ratings than consultant responses (76 [67-85] vs. 72 [63-80], p < 0.01). Scores were higher for understandability, helpfulness, and reassurance (all p < 0.01). ChatGPT was preferred in four of six questions, one showed no difference, and one favored the consultant. Subgroup analyses showed no significant variation based on patient characteristics.

conclusionsIn this exploratory study, women rated ChatGPT's responses as clearer and more reassuring than consultant answers. These findings reflect patient perceptions in a limited setting and should be interpreted with caution. While LLMs may have a supportive role in patient education, their use must remain secondary to expert clinical care and subject to careful oversight.

Indexed as

GynecologyLarge Language ModelsUrologyAgedCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansMiddle AgedReproducibility of ResultsSurveys and Questionnairesartificial intelligencedecision‐makingnatural language processingpatient educationUrogynecology

Identifiers

PMID41128396
PMCPMC13356474

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.