ArticleGynecologic and obstetric investigation2025
The Role of Artificial Intelligence in Gynecologic Oncology Decision-Making: A Feasibility Study.
Article in Gynecologic and obstetric investigation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- 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
- Article
- The role of large language models in the diagnosis and management of obstetric patients: a pilot feasibility study using simulated obstetric cases.Archives of gynecology and obstetrics · 2026Article
- The Role of Large Language Models in the Promotion of Minimally Invasive Interventional Radiologic Methods in Gynecology and Obstetrics.Journal of clinical medicine · 2026Article
- A comparative evaluation of publicly available large language models in the assessment of CTG traces according to the FIGO criteria.Archives of gynecology and obstetrics · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe objective of this study was to examine the potential of artificial intelligence (AI) in gynecologic oncology decision-making.
designA feasibility study was conducted.
participantsFictitious case vignettes of patients with gynecologic carcinomas were used.
settingThe setting was a fictive one.
methodsFictitious case vignettes of gynecologic carcinomas were created and evaluated by physicians with varying levels of professional experience, as well as by language models including ChatGPT 4.0, Google Gemini, and Bing Copilot. Treatment approval decisions were based on standardized clinical and laboratory criteria.
resultsTwo cases of breast cancer, 1 case of ovarian cancer, 1 case of cervical cancer, and 1 case of endometrial cancer were evaluated. All three language models were able to evaluate all clinical cases and make therapy-relevant suggestions, with ChatGPT providing the most clear and concise recommendations that were in 3 cases totally consistent with physician assessments. LIMITATIONS: This study was limited to a feasibility study based on five fictitious case vignettes.
conclusionsThe study demonstrates that AI models, such as ChatGPT, can to some extent evaluate clinical cases, recognize clinical and/or laboratory abnormalities, and make therapy-related suggestions. Despite high overall agreement, differences were predominantly noted in the more complex cases, rendering human interpretation necessary. The findings underscore the benefits of AI in terms of clarity, time efficiency, and cost-effectiveness. Future research should further explore the application of AI to real patient data and development of hybrid decision models to optimize integration into clinical practice.
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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.