RadAnnotate: Large Language Models for Efficient and Reliable Radiology Report Annotation

ArXi:2603.16002v1 Announce Type: cross Radiology report annotation is essential for clinical NLP, yet manual labeling is slow and costly. We present RadAnnotate, an LLM-based framework that studies retrieval-augmented synthetic reports and confidence-based selective automation to reduce expert effort for labeling in RadGraph. We study RadGraph-style entity labeling (graph nodes) and leave relation extraction (edges) to future work.