Integrating large language models into prostate cancer training: evidence from comparative benchmarking and a pilot randomized trial

Large language models (LLMs) are increasingly explored as tools for medical education. However, evidence remains limited regarding their pedagogical quality and real-world utility in prostate cancer teaching within urology residency training.

We conducted a two-phase study. Phase 1 benchmarked three LLMs (ChatGPT-4o, DeepSeek R1, and Gemini 2.0) on a structured 40-item prostate cancer education question bank using standardized prompts and blinded expert ratings. Phase 2 implemented the top-performing model in a pilot randomized teaching trial among urology residents (n = 34) using stratified block randomization based on a pre-admission standardized test score, with allocation concealment implemented through a centralized web-based system. Both groups received identical offline instruction with a time-matched lecture structure. The control group committed to avoiding LLM use for course-related questions during the teaching period.

DeepSeek R1 ranked highest in expert ratings, with clearer advantages on higher-order and innovation-oriented questions. In the pilot randomized teaching trial (n = 34), the AI-assisted group achieved higher closed-book examination scores than controls (68.47 ± 12.78 vs. 57.91 ± 10.47; MD 10.56, 95% CI 2.39–18.73; p = 0.013). Improvements were most evident in Multiple-Choice Questions(MCQs) (MD 9.27, 95% CI 4.37–14.17; p < 0.001) and research items (MD 3.32, 95% CI 1.72–4.92; p < 0.001), whereas Multidisciplinary Team(MDT) case analysis showed no clear difference. Student and instructor feedback was generally positive.

LLM-assisted teaching was associated with higher knowledge-based examination performance, especially for MCQ-style and innovation-focused content, while effects on MDT reasoning remain uncertain. These preliminary findings suggest that carefully guided LLM use may support residency teaching, but larger multicenter studies and structured verification workflows are needed to confirm effectiveness and generalizability.