ArticleCureus2024
Performance of ChatGPT vs. HuggingChat on OB-GYN Topics.
Article in Cureus, 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Comparative analysis of ChatGPT 3.5 and ChatGPT 4 obstetric and gynecological knowledge.Scientific reports · 2025Article
- An evaluation of ChatGPT and Bard (Gemini) in the context of biological knowledge retrieval.Access microbiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background While large language models show potential as beneficial tools in medicine, their reliability, especially in the realm of obstetrics and gynecology (OB-GYN), is not fully comprehended. This study seeks to measure and contrast the performance of ChatGPT and HuggingChat in addressing OB-GYN-related medical examination questions, offering insights into their effectiveness in this specialized field. Methods ChatGPT and HuggingChat were subjected to two standardized multiple-choice question banks: Test 1, developed by the National Board of Medical Examiners (NBME), and Test 2, gathered from the Association of Professors of Gynecology & Obstetrics (APGO) Web-Based Interactive Self-Evaluation (uWISE). Responses were analyzed and compared for correctness. Results The two-proportion z-test revealed no statistically significant difference in performance between ChatGPT and HuggingChat on both medical examinations. For Test 1, ChatGPT scored 90%, while HuggingChat scored 85% (p = 0.6). For Test 2, ChatGPT correctly answered 70% of questions, while HuggingChat correctly answered 62% of questions (p = 0.4). Conclusion Awareness of the strengths and weaknesses of artificial intelligence allows for the proper and effective use of its knowledge. Our findings indicate that there is no statistically significant difference in performance between ChatGPT and HuggingChat in addressing medical inquiries. Nonetheless, both platforms demonstrate considerable promise for applications within the medical domain.
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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.