Enhancing perinatal health patient information through ChatGPT – An accuracy study
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.
Authors
- U. Winterfeld
- Émeline Maisonneuve (ORCID: https://orcid.org/0000-0001-5765-4468)
- Michael Ceulemans (ORCID: https://orcid.org/0000-0002-4130-5869)
- David Baud (ORCID: https://orcid.org/0000-0001-9914-6496)
- Stéphanie Baggio (ORCID: https://orcid.org/0000-0002-5347-5937)
- E. Gerbier
- Léo Pomar (ORCID: https://orcid.org/0000-0002-3021-8129)
- Guillaume Favre (ORCID: https://orcid.org/0000-0001-6181-3126)
- Hélène Legardeur (ORCID: https://orcid.org/0000-0002-1124-847X)
- Alice Panchaud (ORCID: https://orcid.org/0000-0001-6086-2401)
- P. de Vries
- C. Pena-Reyes
Institutions
- University of Bern (CH)
- SIB Swiss Institute of Bioinformatics (CH)
- Radboud University Nijmegen (NL)
- University of Fribourg (CH)
- HES-SO University of Applied Sciences and Arts Western Switzerland (CH)
- Centre Hospitalier Universitaire Vaudois (CH)
- Radboud University Medical Center (NL)
- University of Lausanne (CH)
- KU Leuven (BE)
Publication Details
- Journal
- PEC Innovation
- Published
- 2025-02-11
- DOI
- https://doi.org/10.1016/j.pecinn.2025.100381
- Citations
- 8
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 5.23
Funders
- KU Leuven