验证药物基因组学生成式人工智能查询提示词：基于检索增强生成（RAG）
Validating Pharmacogenomics Generative Artificial Intelligence Query Prompts Using Retrieval-Augmented Generation (RAG)
AI总结：
本研究验证了Sherpa Rx工具，通过整合CPIC和PharmGKB数据及RAG技术，在药物基因组学查询中显著提升响应准确性与完整性，优于ChatGPT-4omini。
摘要：
本研究评估了Sherpa Rx，一种利用大型语言模型和检索增强生成（RAG）技术用于药物基因组学的人工智能工具，以验证其在关键响应指标上的性能。Sherpa Rx整合了临床药物遗传学实施联盟（CPIC）指南与药物基因组学知识库（PharmGKB）数据，以生成上下文相关的响应。研究使用了涵盖26项CPIC指南的数据集（N=260个查询）来评估药物-基因相互作用、剂量建议和治疗意义。在第一阶段，仅嵌入CPIC数据。第二阶段额外整合了PharmGKB内容。响应在准确性、相关性、清晰度、完整性（采用5点李克特量表）和召回率方面进行评分。采用Wilcoxon符号秩检验比较第一阶段与第二阶段之间以及第二阶段与ChatGPT-4omini之间的准确性。一项包含20个问题的测验评估了该工具相对于其他模型的实际应用性。在第一阶段（N=260）中，Sherpa Rx表现出高性能：准确性4.9，相关性5.0，清晰度5.0，完整性4.8，召回率0.99。子集分析（N=20）显示准确性（第二阶段对第一阶段子集：4.6对4.4）和完整性（5.0对4.8）均有提升。ChatGPT-4omini在相关性（5.0）和清晰度（4.9）方面表现相当，但在准确性（3.9）和完整性（4.2）方面有所落后。第一阶段与第二阶段之间的差异在统计学上不显著。然而，第二阶段显著优于ChatGPT-4omini。在20题测验中，Sherpa Rx达到了90%的准确率，优于其他模型。整合如CPIC和PharmGKB等额外资源并结合RAG，可增强人工智能的准确性和性能。本研究凸显了像Sherpa Rx这样的生成式人工智能在药物基因组学中的变革潜力，通过提供准确、个性化的响应来改善决策。
英文摘要：
This study evaluated Sherpa Rx, an artificial intelligence tool leveraging large language models and retrieval-augmented generation (RAG) for pharmacogenomics, to validate its performance on key response metrics. Sherpa Rx integrated Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines with Pharmacogenomics Knowledgebase (PharmGKB) data to generate contextually relevant responses. A dataset (N=260 queries) spanning 26 CPIC guidelines was used to evaluate drug-gene interactions, dosing recommendations, and therapeutic implications. In Phase 1, only CPIC data was embedded. Phase 2 additionally incorporated PharmGKB content. Responses were scored on accuracy, relevance, clarity, completeness (5-point Likert scale), and recall. Wilcoxon signed-rank tests compared accuracy between Phase 1 and Phase 2, and between Phase 2 and ChatGPT-4omini. A 20-question quiz assessed the tool's real-world applicability against other models. In Phase 1 (N=260), Sherpa Rx demonstrated high performance of accuracy 4.9, relevance 5.0, clarity 5.0, completeness 4.8, and recall 0.99. The subset analysis (N=20) showed improvements in accuracy (4.6 vs. 4.4, Phase 2 vs. Phase 1 subset) and completeness (5.0 vs. 4.8). ChatGPT-4omini performed comparably in relevance (5.0) and clarity (4.9) but lagged in accuracy (3.9) and completeness (4.2). Differences in accuracy between Phase 1 and Phase 2 was not statistically significant. However, Phase 2 significantly outperformed ChatGPT-4omini. On the 20-question quiz, Sherpa Rx achieved 90% accuracy, outperforming other models. Integrating additional resources like CPIC and PharmGKB with RAG enhances AI accuracy and performance. This study highlights the transformative potential of generative AI like Sherpa Rx in pharmacogenomics, improving decision-making with accurate, personalized responses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
