ERBB2 and neoplasm: Objective: Evaluated zero-shot classification of breast cancer pathology reports, comparing them to supervised machine learning models like random forests, LSTM with attention, and UCSF-BERT. The goal was to assess if LLMs could reduce the need for large-scale data annotations in clinical NLP tasks.Prompt: LLMs were prompted to extract 12 categories of breast cancer pathology information, such as tumour margins, lymph node involvement, and HER2 status, providing single answers in JSON format.