Objective:

To compare the performance of various large language models (LLMs) in generating responses to clinical questions in urology and assess their utility in clinical environments, addressing the existing gap in evidence.

Key Findings:

• DeepSeek-V3 and DeepSeek-R1 are noted for their elaborate, context-aware narratives, sometimes matching human responses, but may lack depth in nuanced clinical scenarios.
• OpenAI O3 mini versions provide robust question-answering capabilities with a focus on reasoning and safety alignment, though they may not always match the depth of DeepSeek models.
• DeepSeek models utilize a Mixture-of-Experts architecture for deeper topical responses, while OpenAI models employ a dense-transformer backbone for concise answers, highlighting their different approaches.
• Inconsistencies and inaccuracies in LLM outputs can pose risks to clinical judgment, particularly in sensitive areas like antibiotic stewardship, necessitating careful scrutiny.

Interpretation:

The comparative analysis highlights the potential of LLMs to assist urologists in clinical decision-making, emphasizing the need for caution due to possible inaccuracies and ethical considerations that could impact patient care.

Limitations:

• Reliability of LLMs has not been consistently proven across all domains, with specific concerns about their performance in sensitive clinical areas.
• Potential biases in training data may propagate healthcare inequities, particularly affecting underrepresented groups.
• Lack of transparency in LLM decision-making can hinder informed consent and accountability, raising ethical concerns in clinical practice.

Conclusion:

While LLMs like DeepSeek and OpenAI O3 mini show promise in enhancing clinical practice, their deployment must be approached with careful consideration of ethical and practical implications, necessitating ongoing evaluation and oversight.