Using GPT-5.4 to clean labels in the CT-RATE chest CT dataset revealed 3.6% discordance with original labels, with radiologists supporting the LLM labels in 74-92% of reviewed cases.
A Review on Medical Image Segmentation: Datasets, Technical Models, Challenges and Solutions
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Large Language Model-Assisted Cleaning of Report-Derived Labels in a Large-Scale Chest CT Dataset
Using GPT-5.4 to clean labels in the CT-RATE chest CT dataset revealed 3.6% discordance with original labels, with radiologists supporting the LLM labels in 74-92% of reviewed cases.