Automated Prediction of Radiological Protocols Using Retrieval Augmented Generation.

Radiological protocol selection is a critical but time-consuming step in clinical workflow, requiring radiologists to match patient indications with an appropriate MRI or CT protocol. Manual selection can be prone to delays or potential errors, and automated approaches must contend with substantial class imbalance, site-specific variation, and evolving nomenclature. We investigated whether a large language model (LLM) can support reliable protocol selection at scale and whether retrievalaugmented generation (RAG) offers operational advantages over direct fine-tuning. Using patient reports collected across three Mayo Clinic sites (Arizona, Florida, and Rochester) spanning six radiological divisions, we trained site-specific Llama 3.2 3B models for use with and without retrieval augmentation. Division-scoped Facebook AI Similarity Search (FAISS) indexes constructed from procedure and diagnosis text were used to supply contextual evidence in the RAG framework. Both fine-tuned non-RAG and RAG-augmented models achieved strong baseline performance across sites. Paired bootstrap analyses revealed that RAG improved macro F1 at two of three sites (Arizona::