ArticleScientific reports2025
Comparative analysis of ChatGPT 3.5 and ChatGPT 4 obstetric and gynecological knowledge.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Large Language Models Meet Gynecologic Ultrasound: Advancing the Characterization of ADNEXal Masses.Journal of imaging · 2026Article
- Article
- [Menstruation on YouTube, Instagram, and TikTok: a content and quality analysis].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2026Article
- Can large language models serve as consultants for forensic cause of death analysis? A multidimensional evaluation.Frontiers in artificial intelligence · 2026Article
- Automated Nuchal Translucency analysis for early prenatal risk stratification.Frontiers in artificial intelligence · 2026Article
- Evaluation and comparison of large language model responses to patient questions after diagnosis of high-risk human papillomavirus infection: an expert-rated digital patient education study.Frontiers in public health · 2026Article
- Application of artificial intelligence in diagnosis and management of fetal growth disorders: a comprehensive review.Frontiers in medicine · 2025Review
- Comparative performance of ChatGPT and DeepSeek in interpreting the 2025 ESICM guidelines on sepsis fluid therapy.Digital healthArticle
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative Pretrained Transformer (GPT) is one of the most ubiquitous large language models (LLMs), employing artificial intelligence (AI) to generate human-like language. Although the use of ChatGPT has been evaluated in different medical specialties, sufficient evidence in the field of obstetrics and gynecology is still lacking. The aim of our study was to analyze the knowledge of the two latest generations of ChatGPT (ChatGPT-3.5 and ChatGPT-4) in the area of obstetrics and gynecology, and thereby to assess their potential applicability in clinical practice. We submitted 352 single-best-answer questions from the Polish Specialty Certificate Examinations in Obstetrics and Gynecology to ChatGPT-3.5 and ChatGPT-4, in both Polish and English. The models' accuracy was evaluated, and performance was analyzed based on question difficulty and language. Statistical analyses were conducted using the Mann-Whitney U test and the chi-square test. The results of the study indicate that both LLMs demonstrate satisfactory knowledge in the analyzed specialties. Nonetheless, we observed a significant superiority of ChatGPT-4 over its predecessor regarding the accuracy of answers. The correctness of answers of both models was associated with the difficulty index of questions. In addition, based on our analysis, ChatGPT should be used in English for optimal performance.
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