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. Here, ERBB2 is linked to breast cancer.