Retrieval-Augmented Generation: Advancing personalized care and research in oncology

Abstract

Retrieval-Augmented Generation (RAG) pairs large language models (LLMs) with recent data to produce more accurate, context-aware outputs. By converting text into numeric embeddings, RAG locates and retrieves relevant “chunks” of data, that along with the query, ground the model’s responses in current, specific information. This process helps reduce outdated or fabricated answers. In oncology, RAG has shown particular promise. Studies have demonstrated its ability to improve treatment recommendations by integrating genetic profiles, strengthened clinical trial matching through biomarker analysis, and accelerated drug development by clarifying model-driven insights. Despite its advantages, RAG depends on high-quality data. Biased or incomplete sources can lead to inaccurate outcomes. Careful implementation and human oversight are crucial for ensuring the effectiveness and reliability of RAG in oncology.