RadAnnotate: Large Language Models for Efficient and Reliable Radiology Report Annotation
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. First, we train entity-specific classifiers on gold-standard reports and characterize their strengths and failure modes across anatomy and observation categories, with uncertain observations hardest to learn. Second, we generate RAG-guided synthetic reports and show that synthetic-only models remain...
| Authors | Saisha Pradeep Shetty, Roger Eric Goldman, Vladimir Filkov |
|---|---|
| Primary category | cs.CL |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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