Performance of Next-Generation Reasoning Models on Self-Assessment Questions for Nephrology Board Recertification

Background: Large Language Models (LLMs) show medical promise. Standard LLMs primarily leverage pattern recognition from vast datasets, whereas newer reasoning models are architecturally designed to enhance multi-step logical inference. However, whether this fundamental difference translates to superior performance by reasoning models in nephrology remains underexplored. We compared cutting-edge reasoning models against standard LLMs using nephrology multiple choice questions. Methods: We used 209 self-assessment questions for nephrology board recertification from the Japanese Society of Nephrology (2014-2023). Reasoning models (OpenAI's o3, o3-2025-04-16; Google's Gemini 2.5 Pro, gemini-2.5-pro-preview-03-25) and standard models (OpenAI's GPT-4o, gpt-4o-2024-11-20; Google's Gemini 2.0 Flash, gemini-2.0-flash-001) were evaluated for accuracy via API. Accuracy was also analyzed by question characteristics (taxonomy, question type, image inclusion, subspecialty) and compared (chi-squared/Fisher's exact tests, p<0.05). Results: Reasoning models o3 (89.5%) and Gemini 2.5 Pro (83.7%) had significantly higher overall accuracy than standard GPT-4o (69.9%) and Gemini 2.0 Flash (62.7%) (all p<0.001). No significant difference was found between the reasoning models (p=0.114). Reasoning models met passing threshold (≥60%) in all 10 years; standard models in 7/10 years. Reasoning models showed superiority for recall, problem-solving, general, clinical, and non-image questions. Subspecialty performance varied; reasoning models generally outperformed standard ones, but no significant differences were noted for interpretation or image questions. Conclusion: Reasoning models significantly outperformed standard LLMs on nephrology multiple choice questions, showing high potential as educational and research support tools in nephrology. Responsible implementation requires further validation and expert oversight.