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Abstract #3377

Using Large Language Models and Retrieval-Augmented Generation in MRI Protocol Selection: Balancing Accuracy and Privacy

CHIH-HSING TANG1, PO-CHIN LIANG1, YUNG-HSUAN YANG2, YU-HAN YANG2, SIN-SYUAN WU1, JING-YAO GAO1, I-LING CHUNG1, JR-CHING HSU1, and WAN-DE HUANG1
1NTU BioMedical Park Hospital, Hsinchu County,, Taiwan, 2NTU Hospital, Taipei County, Taiwan

Synopsis

Keywords: Language Models, Language Models, Retrieval-Augmented Generation(RAG)

Motivation: When medical staff lack clinical experience, they might pick the wrong protocols and parameters, leading to wasted time and resources.

Goal(s): We want to show that combining RAG technology with LLMs can help determine MRI protocols at a level matching seasoned professionals—all while keeping patient privacy intact.

Approach: Using clinical doctors' MRI exam requests as our benchmark, we compared accuracy across different experience levels and professions to see how cloud-based and local LLMs differ in performance.

Results: After adding RAG technology, the cloud-based LLM matched the expertise of experienced radiologists, while the local LLM reached accuracy similar to that of senior radiologic technologists.

Impact: We've proven that RAG-based LLMs are feasible for early MRI decision-making, offering a new tool for learning and error prevention. Cloud-based LLMs and local LLMs each have their strengths in accuracy and privacy, but neither is perfect just yet.

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