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

Abstract

PURPOSE: This study aims to evaluate the effect of input format and hyperparameter settings on GPT-5 and explore the contribution of GPT-5 assistance to radiologists' performance in abdominal cases. METHODS: In this prospective study, 86 abdominal cases were evaluated, with GPT-5 evaluated in two deployment contexts: browser-based GPT-5 (default, non-configurable sampling settings) and GPT-5 accessed via the OpenAI application programming interface (API) with different temperature and top-p settings. First, the diagnostic and differential diagnosis performance of browser-based GPT-5 in these cases was assessed using two different input formats: "only visual" and "visual with imaging findings and clinical presentation." Subsequently, its performance was evaluated at varying temperature (0, 0.5, 1, 1.5) and top-p (0, 0.5, 1) values; the values at which the model performs best are considered "optimal settings." Finally, two junior radiologists evaluated the same cases with and without GPT-5 assistance with washout periods. Their performances were compared internally and with that of an abdominal radiologist. RESULTS: < 0.001). Hyperparameter optimization further improved GPT-5 performance, with diagnostic accuracy increasing to 73% at the optimal settings (temperature: 1.5, top-p: 1) and mean differential diagnosis scores improving from 3.44 to 3.84. The radiologists' diagnostic accuracy increased from 73% and 71% without assistance to 87% and 86% with browser-based GPT-5 assistance and further to 94% with GPT-5 with optimal settings assistance. Differential diagnosis performance similarly improved from median scores of 4 (range: 3-5) without assistance to 5 (range: 4-5) with GPT-5 (with optimal settings) assistance. CONCLUSION: Using hyperparameter and input optimization settings with GPT-5 could improve its clinical utility. CLINICAL SIGNIFICANCE: 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.

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