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Objectives <p>Large language models (LLMs) are increasingly being used in medical research and as clinical decision-support tools. This study aimed to compare the accuracy and reliability of responses generated by large language models in response to andrology-related questions.</p> Materials and methods <p>Seventy questions concerning diagnosis, treatment, and general information were developed on the basis of the 2024 Andrology Guidelines of the European Association of Urology (EAU). These questions were submitted to three large language models, namely ChatGPT-4, ChatGPT-4o, and Google Gemini 2.0 Flash. The responses were independently evaluated by three senior urologists using a four-point rating scale. A total score (TS) &gt; 9 indicated a good response, 6 ≤ TS ≤ 9 indicated a moderate response, and TS &lt; 6 indicated a poor response. In addition, the self-correction capabilities of the models were evaluated, and changes in response accuracy after re-evaluation were analyzed.</p> Results <p>ChatGPT-4o achieved the highest total scores in the diagnosis and treatment categories (<i>p</i> &lt; 0.001). Google Gemini 2.0 Flash generated the longest responses but demonstrated the lowest accuracy. ChatGPT-4o also showed the greatest improvement following the self-correction process (Cohen’s d = − 1.214, <i>p</i> &lt; 0.01). Fleiss’ kappa coefficient values ranged from 0.61 to 0.80, indicating substantial interrater agreement among the urologists.</p> Conclusion <p>ChatGPT-4o emerged as the most reliable model for andrology-related questions, providing responses that are were highly consistent with current clinical guidelines. The self-correction capabilities of the models improved response accuracy, suggesting that error-awareness mechanisms in large language models have the potential for further refinement. Nevertheless, expert supervision remains essential for the safe implementation of AI-assisted systems in clinical practice.</p>

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The doctors of the future: the competition of ChatGPT-4, ChatGPT-4 omni, and Gemini 2.0 Flash in andrology

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Umit Uysal,
  • Ergun Alma,
  • Adem Altunkol,
  • Hakan Ercil,
  • Mert Hamza Özbilen,
  • Hakan Anıl,
  • Kazım Yelsel,
  • Burak Sağmak,
  • Gökay Çetinkökü

摘要

Objectives

Large language models (LLMs) are increasingly being used in medical research and as clinical decision-support tools. This study aimed to compare the accuracy and reliability of responses generated by large language models in response to andrology-related questions.

Materials and methods

Seventy questions concerning diagnosis, treatment, and general information were developed on the basis of the 2024 Andrology Guidelines of the European Association of Urology (EAU). These questions were submitted to three large language models, namely ChatGPT-4, ChatGPT-4o, and Google Gemini 2.0 Flash. The responses were independently evaluated by three senior urologists using a four-point rating scale. A total score (TS) > 9 indicated a good response, 6 ≤ TS ≤ 9 indicated a moderate response, and TS < 6 indicated a poor response. In addition, the self-correction capabilities of the models were evaluated, and changes in response accuracy after re-evaluation were analyzed.

Results

ChatGPT-4o achieved the highest total scores in the diagnosis and treatment categories (p < 0.001). Google Gemini 2.0 Flash generated the longest responses but demonstrated the lowest accuracy. ChatGPT-4o also showed the greatest improvement following the self-correction process (Cohen’s d = − 1.214, p < 0.01). Fleiss’ kappa coefficient values ranged from 0.61 to 0.80, indicating substantial interrater agreement among the urologists.

Conclusion

ChatGPT-4o emerged as the most reliable model for andrology-related questions, providing responses that are were highly consistent with current clinical guidelines. The self-correction capabilities of the models improved response accuracy, suggesting that error-awareness mechanisms in large language models have the potential for further refinement. Nevertheless, expert supervision remains essential for the safe implementation of AI-assisted systems in clinical practice.