ReviewInternational journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics2025
Exploring the role of artificial intelligence, large language models: Comparing patient-focused information and clinical decision support capabilities to the gynecologic oncology guidelines.
Review in International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating natural non-pharmaceutical therapies into medical tourism: a dynamic health portrait-driven model for proactive older adult health and public health services.Frontiers in public health · 2025Pooled it
- Misaligned by design: evaluating and deploying generative AI for the real-world conditions of primary care: a Nordic and European perspective.Scandinavian journal of primary health care · 2026Article
- Assessing the Accuracy, Completeness, and Reference Quality of GPT-4 and Google for Gynecologic Cancer Information: Comparative Quantitative Study.JMIR cancer · 2026Article
- Leveraging artificial intelligence to streamline documentation and support patient-centered gynecologic oncology outpatient visits.Gynecologic oncology reports · 2026Article
- Feasibility and Concordance of a Large Language Model (ChatGPT-5) as a Clinical Decision Support Tool in Gynecologic Oncology Tumor Boards: A Blinded, Multi-Observer Study.Journal of clinical medicine · 2026Article
- Can ChatGPT Take My Call? evaluating AI in gynecologic oncology telephone triage.Gynecologic oncology reports · 2026Article
- Characterizing HIV-related patient portal preferences among men who have sex with men in the United States.BMC public health · 2026Article
- Evaluation of ChatGPT-5-generated clinical management recommendations for HPV infection and cervical premalignant lesions: a scenario-based expert assessment study.BMC medical informatics and decision making · 2026Article
- Generative AI for patient education in cancer care: A scoping review of evaluation practices and emerging trends.Technical innovations & patient support in radiation oncology · 2026Review
- Physicians' acceptance of large language model-based clinical decision support tools in gynecologic oncology: a technology acceptance model study.Frontiers in digital health · 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
- AI-Driven predictive modeling of cervical intraepithelial neoplasia severity: a comprehensive analysis with clinical adoption frameworks.BMC cancer · 2025Article
- Article
- Artificial intelligence in the diagnosis and management of gynecologic cancer.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025Review
- Exploring the possibilities and limitations of customized large language model to support and improve cervical cancer screening.BMC medical informatics and decision making · 2025Article
- The Role of Medical Therapies in the Management of Cervical Intraepithelial Neoplasia: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
- Exploring the role of artificial intelligence, large language models: Comparing patient-focused information and clinical decision support capabilities to the gynecologic oncology guidelines.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025Review
- Real world perspectives on endometriosis disease phenotyping through surgery, omics, health data, and artificial intelligence.npj women's health · 2025Review
- A bibliometric analysis of artificial intelligence applied to cervical cancer.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gynecologic cancer requires personalized care to improve outcomes. Large language models (LLMs) hold the potential to provide intelligent question-answering with reliable information about medical queries in clear and plain English, which can be understood by both healthcare providers and patients. We aimed to evaluate two freely available LLMs (ChatGPT and Google's Bard) in answering questions regarding the management of gynecologic cancer. The LLMs' performances were evaluated by developing a set questions that addressed common gynecologic oncologic findings from a patient's perspective and more complex questions to elicit recommendations from a clinician's perspective. Each question was presented to the LLM interface, and the responses generated by the artificial intelligence (AI) model were recorded. The responses were assessed based on the adherence to the National Comprehensive Cancer Network and European Society of Gynecological Oncology guidelines. This evaluation aimed to determine the accuracy and appropriateness of the information provided by LLMs. We showed that the models provided largely appropriate responses to questions regarding common cervical cancer screening tests and BRCA-related questions. Less useful answers were received to complex and controversial gynecologic oncology cases, as assessed by reviewing the common guidelines. ChatGPT and Bard lacked knowledge of regional guideline variations, However, it provided practical and multifaceted advice to patients and caregivers regarding the next steps of management and follow up. We conclude that LLMs may have a role as an adjunct informational tool to improve outcomes.
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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.