Prospective quantitative analysis of hyperparameter and input optimization in GPT-5: comparative contribution to radiologist performance in abdominal radiology

This study evaluates GPT-5 performance in a single-source, open-access abdominal case set. In this study, GPT-5 performance improved with structured text inputs and API-based hyperparameter optimization, and large language model (LLM) assistance was associated with improved diagnostic and differential diagnosis performance among junior radiologists. These findings suggest that documenting and standardizing hyperparameter settings (e.g., temperature and top-p) may be important for future LLM-based decision-support applications.