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is often hampered by outdated training data and a lack of explainability, which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.

本研究通过使用 OpenAI 的 GPT-3.5、GPT-4 和 GPT-4o 模型，结合检索增强生成（RAG）和欧洲泌尿外科协会（EAU）最新的 2023 年指南，创建和评估了 UroBot，一种泌尿科专用聊天机器人。在欧洲泌尿外科委员会（EBU）的政策评估中，UroBot-4o 以 88.4% 的平均正确率表现优秀，超过了 GPT-4o 10.8%，而其得分为 77.6%。与文献中报道的泌尿科医生的平均表现相比（68.7%），UroBot 不仅具有临床验证性，还表现出良好的一致性和准确性，展示了其在临床整合中的潜力，并提供了进一步开发 UroBot 的必要代码和说明。

可解释的大型语言模型在欧洲泌尿学指南背景下实现卓越表现：UroBot 研究

Superhuman performance in urology board questions by an explainable  large language model enabled for context integration of the European  Association of Urology guidelines: the UroBot study