ArticleJournal of clinical research in pediatric endocrinology2026
Evaluation of Artificial Intelligence Answers for Short Stature in Paediatric Endocrinology by Paediatric Endocrinologists
Article in Journal of clinical research in pediatric endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial intelligence advancements for orthopaedic clinical reasoning: longitudinal assessment of newer models (ChatGPT-5, Grok-3, Gemini 2.5 Flash) compared to clinicians.Archives of orthopaedic and trauma surgery · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Artificial intelligence (AI) is increasingly used in medicine, including pediatric endocrinology. AI models have the potential to support clinical decision-making, patient education, and guidance. However, their accuracy, reliability, and effectiveness in providing medical information and recommendations remain unclear. The aim was to evaluate and compare the performance of four AI models, ChatGPT, Bard, Microsoft Copilot, and Pi, in answering frequently asked questions related to pediatric endocrinology. Methods: Nine questions commonly asked by parents regarding short stature in pediatric endocrinology were selected, based on literature reviews and expert opinions. These questions were posed to four AI models in both Turkish and English. The AI-generated responses were evaluated by 10 pediatric endocrinologists using a 12-item Likert-scale questionnaire assessing medical accuracy, completeness, guidance, and informativeness. Statistical analyses, including Kruskal-Wallis and post-hoc tests, were conducted to determine significant differences between AI models. Results: Bard outperformed other models in guidance and recommendation categories, excelling in directing users to medical consultation. Microsoft Copilot demonstrated strong medical accuracy but lacked guidance capacity. ChatGPT showed consistent performance in knowledge dissemination, making it effective for patient education. Pi scored the lowest in guidance and recommendations, indicating limited applicability in clinical settings. Significant differences were observed between AI models (p<0.05), particularly in completeness and guidance-related categories. Conclusion: The present study highlights the varying strengths and weaknesses of AI models in an area of pediatric endocrinology. While Bard was effective in guidance, Microsoft Copilot excelled at accuracy, and ChatGPT was informative. Future AI improvements should focus on balancing accuracy and guidance to enhance clinical decision-support and patient education. Tailored AI applications may optimize the role of AI in specialized medical fields.
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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.