RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extraction

Understand

Automated radiology report generation (RRG) holds potential to reduce the workload of radiologists, and recent advances in multimodal large language models (MLLMs) have enabled multimodal chest X-ray (CXR) report generation.

• However, existing MLLMs are computationally expensive, require large-scale training data, and may produce hallucinated content, limiting their practical deployment.
• To address these limitations, we propose RA-RRG, a retrieval-augmented RRG framework that combines multimodal retrieval with large language models (LLMs) to generate radiology reports while reducing hallucinations and computational demands.
• RA-RRG uses LLMs to extract clinically essential key phrases from radiology reports and retrieves relevant phrases given an input image.