Artificial intelligence in reproductive endocrinology: an in-depth longitudinal analysis of ChatGPTv4's month-by-month interpretation and adherence to clinical guidelines for diminished ovarian reserve.

Journal: Endocrine

PMID: 39341951

Abstract

OBJECTIVE: To quantitatively assess the performance of ChatGPTv4, an Artificial Intelligence Language Model, in adhering to clinical guidelines for Diminished Ovarian Reserve (DOR) over two months, evaluating the model's consistency in providing guideline-based responses.

Authors

Tugba Gurbuz

Department of Gynecology and Obstetrics Clinic, Medistate Hospital, Istanbul Nişantaşı University, Istanbul, Turkey.
Oya Gokmen

Department of Gynecology, Obstetrics and In Vitro Fertilization Clinic, Medistate Hospital, Istanbul, Turkey.
Belgin Devranoglu

Department of Obstetrics and Gynecology, Zeynep Kamil Maternity/Children, Education and Training Hospital, Istanbul, Turkey.
Arzu Yurci

IVF Department, Department of Gynecology and Obstetrics, Memorial Bahçelievler Hospital, Istanbul Arel University, Istanbul, Turkey.
Asena Ayar Madenli

Department of Obstetrics and Gynecology, Liv Hospital Vadistanbul, Istanbul, Turkey.

Keywords

Adult Artificial Intelligence Endocrinology Female Guideline Adherence Humans Longitudinal Studies Ovarian Reserve Practice Guidelines as Topic Surveys and Questionnaires

External Resources

View on PubMed Access via DOI PubMed (39341951)

Artificial intelligence in reproductive endocrinology: an in-depth longitudinal analysis of ChatGPTv4's month-by-month interpretation and adherence to clinical guidelines for diminished ovarian reserve.

Abstract

Authors

Keywords

External Resources

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