Evidence map›Paper›PMID 40028463›Full record

ArticlePEC innovation2025

Enhancing perinatal health patient information through ChatGPT - An accuracy study.

P L M de Vries, D Baud, S Baggio, M Ceulemans, G Favre, E Gerbier, H Legardeur, E Maisonneuve, C Pena-Reyes, L Pomar and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PEC innovation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Artificial intelligence in obstetrics and gynecology: Evaluating ChatGPT and Google Gemini in answering patient questions.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
    Article
  5. Article
  6. 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

12 authors.

P L M de VriesDepartment of Gynecology and Obstetrics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
D BaudDepartment of Gynecology and Obstetrics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
S BaggioInstitute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland.
M CeulemansClinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, 3000 Leuven, Belgium.
G FavreDepartment of Gynecology and Obstetrics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
E GerbierDepartment of Gynecology and Obstetrics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
H LegardeurDepartment of Gynecology and Obstetrics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
E MaisonneuveDepartment of Gynecology and Obstetrics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
C Pena-ReyesInstitute for Information and Communication Technologies (IICT), School of Engineering and Management Vaud (HEIG-VD), HES-SO University of Applied Sciences and Arts Western, Switzerland.
L PomarDepartment of Gynecology and Obstetrics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
U WinterfeldSwiss Teratogen Information Service and Clinical Pharmacology Service, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, Lausanne, Switzerland.
A PanchaudInstitute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To evaluate ChatGPT's accuracy as information source for women and maternity-care workers on "nutrition" and "red flags" in pregnancy. Methods: Accuracy of ChatGPT-generated recommendations was assessed by a 5-point Likert scale by eight raters for ten indicators per topic in four languages (French, English, German and Dutch). Accuracy and interrater agreement were calculated per topic and language. Results: For both topics, median accuracy scores of ChatGPT-generated recommendations were excellent (5.0; IQR 4-5) independently of language. Median accuracy scores varied with a maximum of 1 on a 5-point Likert-scare according to question's framing. Overall accuracy scores were 83-89 % for 'nutrition in pregnancy' versus 96-98 % for 'red flags in pregnancy'. Inter-rater agreement was good to excellent for both topics. Conclusion: Although ChatGPT generated accurate recommendations regarding the tested indicators for nutrition and red flags during pregnancy, women should be aware of ChatGPT's limitations such as inconsistencies according to formulation, language and the woman's personal context. Innovation: Despite a growing interest in the potential use of artificial intelligence in healthcare, this is, to the best of our knowledge, the first study assessing potential limitations that may impact accuracy of ChatGPT-generated recommendations such as language and question-framing in key domains of perinatal health.

Indexed as

Artificial intelligenceChatGPTNutritionPatient informationPerinatal health informationPregnancyWarning signs

Identifiers

PMID40028463
PMCPMC11872132

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.