A centralized decision-making support consultation response for fertility preservation in breast cancer patients: benchmark performance of generative large language models in terms of reliability and readability

Background Large language models (LLMs) hold considerable potential in medical and health education; however, their reliability and interpretability in highly sensitive areas and in decision-making remain unclear. This study focuses on four publicly available LLMs and systematically evaluates their applicability in fertility preservation scenarios for breast cancer patients, thereby providing guidance for targeted use. Methods This study utilizes Google Trends to identify and filter information