Influence of structured output constraints on GPT-5-Thinking, Gemini 2.5 Pro, and open-weight LLMs for radiology protocol selection

Key Points

• This research aims to evaluate how structured output constraints influence the performance of various large language models (LLMs) in radiology protocol selection.
• Compared performance of GPT-5-Thinking, Gemini 2.5 Pro, and open-weight LLMs.
• Evaluated effects of structured prompting on each model's output.
• Analyzed protocols selected by proprietary versus open models.
• Gemini 2.5 Pro showed improved performance with structured prompting.
• GPT-5-Thinking showed decreased performance under similar constraints.
• Open-weight models exhibited minimal impact from output constraints, while proprietary models outperformed them in protocol selection.