Retrieval-augmented generation enhances large language model performance on the Japanese orthopedic board examination.
Journal:
Journal of orthopaedic science : official journal of the Japanese Orthopaedic Association
Published Date:
Mar 28, 2025
Abstract
INTRODUCTION: Large language models (LLMs) have shown potential in medical applications. However, their effectiveness in specialized medical domains remains underexplored. The integration of Retrieval-Augmented Generation (RAG) has been proposed to improve these models by reducing hallucinations and enhancing domain-specific information access. Through this evaluation, we aim to assess whether RAG can effectively bridge the gap between LLMs' current capabilities and the accuracy needed for medical use by examining GPT-3.5 Turbo, GPT-4o, and o1-preview on the 2024 Japanese Orthopedic Specialist Examination.
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