ArticleDiagnostic and interventional radiology (Ankara, Turkey)2026
Prospective quantitative analysis of hyperparameter and input optimization in GPT-5: comparative contribution to radiologist performance in abdominal radiology.
Article in Diagnostic and interventional radiology (Ankara, Turkey), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Benchmarking Large Language Models in Acute-on-Chronic Pancreatitis: An Exploratory Study.Diagnostics (Basel, Switzerland) · 2026Article
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5 authors.
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Abstract
purposeThis 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.
methodsIn 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.
resultsWith the "only visual" input format, browser-based GPT-5 achieved a diagnostic accuracy of 12%, increasing to 58% when imaging findings and clinical presentations were provided (
conclusionUsing 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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