Retrieval-Augmented Generation in Radiology: A Scoping Review of Architectures, Imaging Applications, and Directions for Equitable Deployment.

Large language models (LLMs) are increasingly explored in radiology, yet concerns persist regarding hallucination and lack of factual grounding. Retrieval-augmented generation (RAG) seeks to address these limitations by coupling generative models with external knowledge retrieval. We conducted a scoping review to characterize how RAG systems have been applied in radiology and medical imaging. A systematic search of PubMed, Embase, Scopus, IEEE Xplore, and arXiv identified 45 studies implementing RAG-based approaches in radiology-related tasks. In terms of clinical tasks, RAG was most commonly applied to radiology report generation and question answering. Dense retrieval strategies predominated, while sparse, hybrid and proprietary retrieval approaches were less frequent. External knowledge sources most frequently comprised biomedical literature databases and clinical guidelines. Applications were heavily skewed toward chest radiography and X-ray-based tasks, with relatively few studies addressing CT, MRI, PET, ultrasound, or under-represented subspecialties such as pediatric radiology and neuroradiology. Most comparative studies reported task-specific performance gains with RAG over non-retrieval baselines, and a small subset reported performance comparable to trained radiologists or state-of-the-art models. However, hallucinations and errors persisted, and heterogeneity across studies limited the generalizability of these findings. Evaluation practices largely relied on automated accuracy or text-overlap metrics, with limited use of standardized expert evaluation and minimal assessment of safety, bias, computational efficiency, or clinical utility. Overall, while RAG shows promise for improving factual grounding in radiology AI, current evaluation paradigms likely overestimate real-world clinical readiness. Future work should prioritize retrieval quality, clinically grounded evaluation, safety-critical error analysis, bias assessment, and deployment-relevant efficiency metrics to enable responsible clinical translation.