A comparative evaluation of publicly available large language models in the assessment of CTG traces according to the FIGO criteria
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) struggle with interpreting cardiotocography (CTG) traces for fetal monitoring. Current AI tools show significant limitations in accurately classifying CTG results according to established medical criteria.
Area Of Science
- Medical Technology
- Artificial Intelligence in Healthcare
- Fetal Monitoring
Background
- Cardiotocography (CTG) is vital for fetal surveillance but suffers from interpretation variability.
- Artificial intelligence (AI), specifically large language models (LLMs), presents an opportunity to enhance diagnostic consistency and reduce clinician burden.
Purpose Of The Study
- To assess and compare the diagnostic accuracy of multiple LLMs for cardiotocography interpretation.
- Evaluation based on the Federation of Gynecology and Obstetrics (FIGO 2015) criteria.
Main Methods
- Sixty cardiotocography (CTG) traces, pre-classified by expert clinicians, were analyzed.
- Four LLMs (Chat-GPT-4.0, Google Gemini, Bing Copilot, DeepSeek) were tested in a two-phase protocol using normal and abnormal CTG traces presented as screenshots.
Main Results
- DeepSeek and Google Gemini demonstrated inability or poor performance in CTG interpretation.
- Chat-GPT-4.0 showed partial success, while Bing Copilot accurately interpreted normal CTGs but failed on abnormal ones.
Conclusions
- Current large language models exhibit significant limitations in reliably interpreting cardiotocography (CTG) traces.
- Further development is needed for AI tools to meet clinical standards for fetal monitoring interpretation.
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