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February 19, 2026Diagnostic and Interventional Radiology2 citationsOpen Access

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

EÇEren ÇamurTCTuray CesurYGYasin Celal Güneş

Key Points

  • This research aims to assess how hyperparameter and input optimization affect GPT-5's diagnostic capabilities in abdominal radiology.
  • Performed a quantitative evaluation using open-access abdominal case set
  • Utilized structured text inputs and API-based hyperparameter optimization
  • Compared performance metrics among junior radiologists with LLM assistance
  • GPT-5 performance improved with structured text inputs
  • API-based hyperparameter tuning contributed to better diagnostic outcomes
  • Junior radiologists showed enhanced differential diagnosis capabilities

Abstract

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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Cite This Study

Çamur et al. (2026) studied this question.

synapsesocial.com/papers/6996a768ecb39a600b3ed0c6https://doi.org/10.4274/dir.2026.263762
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