Medical Knowledge Simplification for Patients in the Era of LLMs: A Case Study on Diabetes

2026-09-14

AI Summary

研究使用基于大型语言模型的MediClear系统简化糖尿病相关医学知识，通过检索增强生成技术提高患者理解度，实验证明有效降低了阅读难度并获得用户满意。

Abstract

Complex medical information is often difficult for patients to understand, making effective medical knowledge simplification essential for improving patient comprehension, informed decision-making, and health outcomes. Recent advances in large language models (LLMs) provide a promising approach for simplifying complex medical information into patient-friendly language; however, their effectiveness in real-world patient education remains insufficiently explored through human evaluation. To investigate their practical effectiveness, this paper presents a case study on diabetes knowledge simplification through the implementation and evaluation of MediClear, an LLM-based medical knowledge simplification system enhanced with Retrieval-Augmented Generation (RAG). Public diabetes-related articles from Diabetes Australia, WHO, American Diabetes Association (ADA), NIDDK, and AIHW are indexed in the RAG knowledge base to retrieve clinically grounded information, which is then simplified by the LLM into accessible patient explanations. We evaluate the generated responses using standard readability metrics, including the Flesch-Kincaid Grade Level (FKGL), and conduct a human study involving 10 participants. Results show that MediClear consistently reduces the reading level of generated responses to the recommended patient literacy range while achieving high user satisfaction and willingness for future use. This case study demonstrates the potential of LLMs to improve the accessibility of medical knowledge for patient education.

Problem

• Medical Knowledge Simplification
• Patient Comprehension
• Large Language Models
• Readability Metrics
• Patient Education

Innovation

• Large Language Models
• Retrieval-Augmented Generation
• Medical Knowledge Simplification
• Patient Education