ArticleEndocrine2024
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.
Article in Endocrine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in ovarian pathophysiology and management: a systematic review and meta-analysis.Journal of ovarian research · 2026Pooled it
- Article
- Using AI in Forward-Backward Translation of Questionnaires for Men Invited to Prostate Cancer Screening: Methodological Study.JMIR formative research · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo 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.
designA longitudinal study design was employed to evaluate ChatGPTv4's response accuracy and completeness using a structured questionnaire at baseline and at a two-month follow-up.
settingChatGPTv4 was tasked with interpreting DOR questionnaires based on standardized clinical guidelines.
participantsThe study did not involve human participants; the questionnaire was exclusively administered to the ChatGPT model to generate responses about DOR.
methodsA guideline-based questionnaire with 176 open-ended, 166 multiple-choice, and 153 true/false questions were deployed to rigorously assess ChatGPTv4's ability to provide accurate medical advice aligned with current DOR clinical guidelines. AI-generated responses were rated on a 6-point Likert scale for accuracy and a 3-point scale for completeness. The two-phase design assessed the stability and consistency of AI-generated answers over two months.
resultsChatGPTv4 achieved near-perfect scores across all question types, with true/false questions consistently answered with 100% accuracy. In multiple-choice queries, accuracy improved from 98.2 to 100% at the two-month follow-up. Open-ended question responses exhibited significant positive enhancements, with accuracy scores increasing from an average of 5.38 ± 0.71 to 5.74 ± 0.51 (max: 6.0) and completeness scores from 2.57 ± 0.52 to 2.85 ± 0.36 (max: 3.0). It underscored the improvements as significant (p < 0.001), with positive correlations between initial and follow-up accuracy (r = 0.597) and completeness (r = 0.381) scores. LIMITATIONS: The study was limited by the reliance on a controlled, albeit simulated, setting that may not perfectly mirror real-world clinical interactions.
conclusionChatGPTv4 demonstrated exceptional and improving accuracy and completeness in handling DOR-related guideline queries over the studied period. These findings highlight ChatGPTv4's potential as a reliable, adaptable AI tool in reproductive endocrinology, capable of augmenting clinical decision-making and guideline development.
Indexed as
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39341951What OpenQuestion holds
Registered trials
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.