ArticleComputational and structural biotechnology journal2025
Artificial intelligence-large language models (AI-LLMs) for reliable and accurate cardiotocography (CTG) interpretation in obstetric practice.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Fetal health state detection method based on parameters efficient ensembling of deep learning.Frontiers in public health · 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
- Review
- Comparative analysis of ChatGPT 3.5 and ChatGPT 4 obstetric and gynecological knowledge.Scientific reports · 2025Article
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Authors and funding
13 authors.
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Abstract
Background: Accurate cardiotocography (CTG) interpretation is vital for the monitoring of fetal well-being during pregnancy and labor. Advanced artificial intelligence (AI) tools such as AI-large language models (AI-LLMs) may enhance the accuracy of CTG interpretation, but their potential has not been extensively evaluated. Objective: This study aimed to assess the performance of three AI-LLMs (ChatGPT-4o, Gemini Advanced, and Copilot) in CTG image interpretation, compare their results to those of junior (JHDs) and senior human doctors (SHDs), and evaluate their reliability in clinical decision-making. Study design: Seven CTG images were interpreted by the three AI-LLMs, five SHDs, and five JHDs, with the evaluations scored by five blinded maternal-fetal medicine experts using a Likert scale for five parameters (relevance, clarity, depth, focus, and coherence). The homogeneity of the expert ratings and group performances were statistically compared. Results: ChatGPT-4o scored 77.86, outperforming the Gemini Advanced (57.14), Copilot (47.29), and JHDs (61.57). Its performance closely approached that of the SHDs (80.43), with no statistically significant difference between the two (p > 0.05). ChatGPT-4o excelled in the depth parameter and was only marginally inferior to the SHDs regarding the other parameters. Conclusion: ChatGPT-4o demonstrated superior performance among the AI-LLMs, surpassed JHDs in CTG interpretation, and closely matched the performance level of SHDs. AI-LLMs, particularly ChatGPT-4o, are promising tools for assisting obstetricians, improving diagnostic accuracy, and enhancing obstetric patient care.
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