Artificial intelligence-large language models (AI-LLMs) for reliable and accurate cardiotocography (CTG) interpretation in obstetric practice

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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