Inferring cancer disease response from radiology reports using large language models with data augmentation and prompting

Abstract

Large clinical language models demonstrate potential to infer cancer disease response from radiology reports at scale. Data augmentation techniques are useful to further improve performance. Prompt-based fine-tuning can significantly reduce the size of the training dataset.