现代生成人工智能技术，如检索增强生成（RAG），可用于支持精准肿瘤治疗讨论。专家会定期审查已发表的文献，以获取劳动密集型过程中治疗的证据和建议。 RAG 管道可以通过将这些出版物中的文本块提供给现成的大语言模型 (LLM) 来帮助减少这项工作，使其无需任何微调即可回答相关问题。通过从可信数据源 (OncoKB) 检索治疗关系，并通过向未经训练的 Llama 2 模型提出简单问题（可访问相关摘要）来重现超过 80% 的治疗关系，从而演示了这种潜在的应用。

Modern generative artificial intelligence techniques like retrieval-augmented generation (RAG) may be applied in support of precision oncology treatment discussions. Experts routinely review published literature for evidence and recommendations of treatments in a labor-intensive process. A RAG pipeline may help reduce this effort by providing chunks of text from these publications to an off-the-shelf large language model (LLM), allowing it to answer related questions without any fine-tuning. This potential application is demonstrated by retrieving treatment relationships from a trusted data source (OncoKB) and reproducing over 80% of them by asking simple questions to an untrained Llama 2 model with access to relevant abstracts.