Low back pain (LBP) is a leading cause of disability globally. Following the onset of LBP and subsequent treatment, adequate patient education is crucial for improving functionality and long-term outcomes. Despite advancements in patient education strategies, significant gaps persist in delivering personalized, evidence-based information to patients with LBP. Recent advancements in large language models (LLMs) and generative artificial intelligence (GenAI) have demonstrated the potential to enhance patient education. However, their application and efficacy in delivering educational content to patients with LBP remain underexplored and warrant further investigation. In this study, we introduce a novel approach utilizing LLMs with Retrieval-Augmented Generation (RAG) and few-shot learning to generate tailored educational materials for patients with LBP. Physical therapists manually evaluated our model responses for redundancy, accuracy, and completeness using a Likert scale. In addition, the readability of the generated education materials is assessed using the Flesch Reading Ease score. The findings demonstrate that RAG-based LLMs outperform traditional LLMs, providing more accurate, complete, and readable patient education materials with less redundancy. Having said that, our analysis reveals that the generated materials are not yet ready for use in clinical practice. This study underscores the potential of AI-driven models utilizing RAG to improve patient education for LBP; however, significant challenges remain in ensuring the clinical relevance and granularity of content generated by these models.

翻译：下背痛（LBP）是全球范围内导致残疾的主要原因。在LBP发作及后续治疗过程中，充分的患者教育对于改善功能状态和长期预后至关重要。尽管患者教育策略已取得进展，但在向LBP患者提供个性化、循证信息方面仍存在显著差距。近期大语言模型（LLMs）和生成式人工智能（GenAI）的发展显示出提升患者教育质量的潜力。然而，这些技术在向LBP患者提供教育内容方面的应用效果仍未得到充分探索，值得进一步研究。本研究提出一种创新方法，结合检索增强生成（RAG）技术和少样本学习的大语言模型，为LBP患者生成定制化教育材料。物理治疗师采用李克特量表对模型生成响应的冗余度、准确性和完整性进行人工评估。同时，使用Flesch易读性指数评估生成教育材料的可读性。研究结果表明，基于RAG的大语言模型优于传统大语言模型，能提供更准确、完整、可读性更高的患者教育材料，且冗余度更低。尽管如此，我们的分析显示生成的材料尚未达到临床应用标准。本研究强调了采用RAG技术的人工智能模型在改善LBP患者教育方面的潜力，但如何确保这些模型生成内容的临床相关性和内容精细度仍面临重大挑战。