  Large Language Models (LLMs) are revolutionizing medical Question-Answering
(medQA) through extensive use of medical literature. However, their performance
is often hampered by outdated training data and a lack of explainability, which
limits clinical applicability. This study aimed to create and assess UroBot, a
urology-specialized chatbot, by comparing it with state-of-the-art models and
the performance of urologists on urological board questions, ensuring full
clinician-verifiability. UroBot was developed using OpenAI's GPT-3.5, GPT-4,
and GPT-4o models, employing retrieval-augmented generation (RAG) and the
latest 2023 guidelines from the European Association of Urology (EAU). The
evaluation included ten runs of 200 European Board of Urology (EBU) In-Service
Assessment (ISA) questions, with performance assessed by the mean Rate of
Correct Answers (RoCA). UroBot-4o achieved an average RoCA of 88.4%, surpassing
GPT-4o by 10.8%, with a score of 77.6%. It was also clinician-verifiable and
exhibited the highest run agreement as indicated by Fleiss' Kappa (k = 0.979).
By comparison, the average performance of urologists on board questions, as
reported in the literature, is 68.7%. UroBot's clinician-verifiable nature and
superior accuracy compared to both existing models and urologists on board
questions highlight its potential for clinical integration. The study also
provides the necessary code and instructions for further development of UroBot.
