P.121 Bridging the evidence gap: RAG-enabled LLMs in neuroimaging decision support

Abstract

Background: Large language models (LLMs) offer potential for clinical decision support but may not fully adhere to current guidelines. Retrieval-augmented generation (RAG) may address this gap by dynamically incorporating external knowledge. This study evaluated LLM adherence with and without RAG to Canadian neuroimaging guidelines. Methods: A novel RAG framework was developed that integrated Canadian Association of Radiologists (CAR) Diagnostic Imaging Referral Guidelines with GPT-4o and o1 models. Clinical scenarios were curated to represent various central nervous system conditions, such as acute stroke, subarachnoid hemorrhage, and multiple sclerosis. Models were prompted with the clinical scenarios, and responses were scored for adherence to the CAR imaging recommendations. Results: Overall, 300 clinical scenarios were used to prompt each model. Adherence rates were 83.8% for GPT-4o, 94.0% for GPT-4o+RAG, 85.5% for o1, and 93.2% for o1+RAG. A Kruskal-Wallis test (H(3)=44.1, p<0.001) identified significant differences among models. Post-hoc comparisons showed RAG-enabled LLMs significantly outperformed standalone models (p<0.001). No significant differences were observed between GPT-4o and o1 without RAG (p=0.531), or between GPT-4o+RAG and o1+RAG (p=0.532). Conclusions: RAG integration significantly improved LLM adherence to Canadian neuroimaging guidelines, even when baseline models demonstrated moderate performance. Future work should validate and explore broader applications of RAG-enabled tools to advance evidence-based care.

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