Using Retrieval-Augmented Generation to Capture Molecularly-Driven Treatment Relationships for Precision Oncology.

Modern generative artificial intelligence techniques like retrievalaugmented 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. [ABSTRACT FROM AUTHOR]