ArticleCureus2026
The OB-GYN Take on GPT: Objective Assessment of Artificial Intelligence Models in Patient Education.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs), a subset of generative artificial intelligence, have garnered significant attention in healthcare. While existing literature has examined the role of LLMs in medical education, primarily focusing on their ability to answer examination questions, few studies have investigated their potential in patient education and assessed the role of LLMs in medical education, few studies evaluate their role in patient education, and none have specifically evaluated their use in OB-GYN. Using a standardized rubric, we assessed the responses of three LLMs and the American College of Obstetricians and Gynecologists (ACOG) on 10 clinical questions that reflect common patient concerns in OB-GYN, which broadly represent common patient queries in OB-GYN. CoPilot by Microsoft demonstrated higher scores by expert reviewers; however, evaluators were more likely to recommend ACOG frequently asked questions (FAQs) or ChatGPT to patients when providing clinical counseling. This study supports the assertion that LLMs have a role in patient education and counseling in OB-GYN. .
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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.