ArticleArchives of gynecology and obstetrics2025
A comparative evaluation of publicly available large language models in the assessment of CTG traces according to the FIGO criteria.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- Performance of next-generation AI chatbots in colorectal cancer knowledge assessment: a comparative pilot study of ChatGPT-5.1, Gemini-3Pro Preview, DeepSeek-V3.2, Kimi K2 Thinking, Qwen3-Max and Claude Opus 4.5.Updates in surgery · 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
- Performance of next-generation AI chatbots in gynecological knowledge assessment: a comparative pilot study of ChatGPT-5, Gemini-3, DeepSeek-V3.2, and Claude-4.5-Opus.Archives of gynecology and obstetrics · 2026Article
- Fetal health state detection method based on parameters efficient ensembling of deep learning.Frontiers in public health · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiotocography (CTG) remains a cornerstone in fetal monitoring, but its interpretation is subject to considerable inter- and intra-observer variability. Artificial intelligence (AI) tools, particularly large language models (LLMs), offer potential to improve diagnostic consistency and reduce clinician workload.
objectivesThis study aims to evaluate and compare the accuracy of various LLMs in CTG interpretation based on Federation of Gynecology and Obstetrics (FIGO 2015) criteria. STUDY
designAn analysis of sixty CTG traces previously classified by clinicians at the University Hospital Basel according to FIGO guidelines was conducted. In a two-run protocol, 30 normal CTG traces were initially presented as screenshots to Chat-GPT-4.0, Google Gemini, Bing Copilot, and DeepSeek. Subsequently, the LLMs that demonstrated adequate interpretation of normal CTGs were tasked to classify another 30 suspicious or pathological CTG traces. Each LLM was asked to classify each CTG trace as normal or abnormal.
resultsDeepSeek was unable to interpret CTGs and was excluded. Google Gemini showed poor performance (6.7%) on normal CTGs. Chat-GPT-4.0 partially succeeded in correctly classifying the provided CTG traces as normal (46.7%) or abnormal (50%). Bing Copilot accurately interpreted normal CTGs (96.6%) but failed on abnormal ones (0%).
conclusionsLLMs show major limitations in the interpretation of CTG traces according to the FIGO criteria.
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