Superhuman performance on urology board questions using an explainable language model enhanced with European Association of Urology guidelines.

Background: Large language models encode clinical knowledge and can answer medical expert questions out-of-the-box without further training. However, this zero-shot performance is limited by outdated training data and lack of explainability impeding clinical translation. We aimed to develop a urology-specialized chatbot (UroBot) and evaluate it against state-of-the-art models as well as historical urologists' performance in answering urological board questions in a fully clinician-verifiable manner. Materials and methods: We developed UroBot, a software pipeline based on the GPT-3.5, GPT-4, and GPT-4o models by OpenAI, utilizing retrieval augmented generation and the 2023 European Association of Urology guidelines. UroBot was benchmarked against the zero-shot performance of GPT-3.5, GPT-4, GPT-4o, and Uro_Chat. The evaluation involved 10 runs with 200 European Board of Urology in-service assessment questions, with the performance measured by the mean rate of correct answers (RoCA). Results: UroBot-4o achieved the highest RoCA, with an average of 88.4%, outperforming GPT-4o (77.6%) by 10.8%. Besides, it is clinician-verifiable and demonstrated the highest level of agreement between runs as measured by Fleiss' kappa (κ = 0.979). In comparison, the average performance of urologists on urological board questions is 68.7% as reported by the literature. Conclusions: UroBot is a clinician-verifiable and accurate software pipeline and outperforms published models and urologists in answering urology board questions. We provide code and instructions to use and extend UroBot for further development.