标题： 基于可验证临床计算器及其元数据的LLM驱动的临床计算器聊天机器人 显示英文标题

标题： An LLM-Powered Clinical Calculator Chatbot Backed by Verifiable Clinical Calculators and their Metadata

摘要： 临床计算器被广泛使用，大型语言模型（LLMs）使得通过自然语言与它们互动成为可能。 我们展示了一个专门构建的聊天机器人，它通过LLM工具和关于这些计算器的元数据来利用可验证的临床计算器的软件实现。 我们在四个自然语言对话工作负载上将聊天机器人的响应准确性与未经辅助的现成LLM进行了比较。 我们的聊天机器人在询问计算器元数据内容的查询中达到了100%的准确性，并且在用完整句子（86.4% vs. 61.8%）或医学缩写（79.2% vs. 62.0%）提示时，显示出临床计算准确性的显著提高。 当用完整句子提示时，它消除了计算错误（0% vs. 16.8%），并且在用医学缩写提示时大大减少了计算错误（2.4% vs. 18%）。 虽然我们的聊天机器人尚未准备好用于临床使用，但这些结果表明在减少错误计算结果方面取得了进展。 显示英文摘要

摘要： Clinical calculators are widely used, and large language models (LLMs) make it possible to engage them using natural language. We demonstrate a purpose-built chatbot that leverages software implementations of verifiable clinical calculators via LLM tools and metadata about these calculators via retrieval augmented generation (RAG). We compare the chatbot's response accuracy to an unassisted off-the-shelf LLM on four natural language conversation workloads. Our chatbot achieves 100% accuracy on queries interrogating calculator metadata content and shows a significant increase in clinical calculation accuracy vs. the off-the-shelf LLM when prompted with complete sentences (86.4% vs. 61.8%) or with medical shorthand (79.2% vs. 62.0%). It eliminates calculation errors when prompted with complete sentences (0% vs. 16.8%) and greatly reduces them when prompted with medical shorthand (2.4% vs. 18%). While our chatbot is not ready for clinical use, these results show progress in minimizing incorrect calculation results.