ArticleArchives of gynecology and obstetrics2025
Large language models and women's health: a digital companion for informed decision-making.
Article in Archives of gynecology and obstetrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
2 citing papers in PubMed.
- The Role of Large Language Models in Hormonal Contraception Consultation.Journal of clinical medicine · 2026Article
- FAIR-Birth: Development and Feasibility Testing of an AI-Supported Advance Birth Planning Application for Midwifery-Led Antenatal Care-A Mixed-Methods Study Protocol.Healthcare (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT, in gynecology and obstetrics has the potential to significantly transform patient care. These AI-driven tools provide continuous access to information on sensitive health topics such as menstruation, contraception, pregnancy, and childbirth, thereby strengthening patient knowledge and autonomy. By offering easily comprehensible medical information at any time, ChatGPT enables patients to better prepare for medical consultations and ask informed questions without hesitation. This fosters a more balanced, partnership-based dialog between patients and healthcare professionals. At the same time, the use of ChatGPT in medicine presents certain limitations and challenges. While it can provide general information, it lacks the ability to assess individual health conditions or support personalized medical decision-making. Additionally, concerns regarding data privacy and the potential for misunderstandings must be carefully considered. As a result, ChatGPT should not replace medical consultations but rather serve as a complementary resource to support patient education and decision-making. Overall, ChatGPT introduces new opportunities for communication in gynecology and obstetrics. Future research and technological advancements may help further refine its capabilities, making it a more tailored and reliable tool for patients while ensuring its safe and responsible integration into healthcare systems.
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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.