MOSAIC achieves mean macro F1 of 88 on chest X-ray report classification across five datasets in four languages using a 4B-parameter open model with low GPU memory and few-shot or light fine-tuning options.
A scoping review of large language model based approaches for information extraction from radiology reports
2 Pith papers cite this work, alongside 68 external citations. Polarity classification is still indexing.
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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.
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MOSAIC: A Multilingual, Taxonomy-Agnostic, and Computationally Efficient Approach for Radiological Report Classification
MOSAIC achieves mean macro F1 of 88 on chest X-ray report classification across five datasets in four languages using a 4B-parameter open model with low GPU memory and few-shot or light fine-tuning options.
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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.