大模型
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大语言模型时代的患者医学知识简化：一项糖尿病案例研究
该论文提出 MediClear，一个结合检索增强生成（RAG）的 LLM 医学知识简化系统，面向糖尿病患者教育。系统将 Diabetes Australia、WHO、ADA、NIDDK、AIHW 的公开糖尿病文章索引为知识库，检索临床依据信息后由 LLM 简化为患者可读的解释。评估采用 Flesch-Kincaid Grade Level 等可读性指标，并开展 10 人人类研究。结果显示生成回答的阅读难度稳定降至推荐的患者识字范围，用户满意度与未来使用意愿较高。作者认为该案例展示了 LLM 提升医学知识可及性的潜力，但样本规模有限，尚需更大规模验证。
AI推荐理由 arXiv 预印本，来源可信、摘要信息明确：提出基于 LLM+RAG 的糖尿病知识简化系统 MediClear，索引 Diabetes Australia、WHO、ADA、NIDDK、AIHW 等公开文章，用 FKGL 等可读性指标评估，并做了 10 人的人类研究，报告可读性降至推荐患者识字范围且满意度较高。局限在于仅为单一糖尿病案例、人类研究样本仅 10 人，未提供临床结局或更大规模验证，因此实际效果仍属初步。
62
正文
Computer Science > Artificial Intelligence
arXiv:2609.15129 (cs)
[Submitted on 14 Sep 2026]
Title:Medical Knowledge Simplification for Patients in the Era of LLMs: A Case Study on Diabetes
Authors:Pallika Kafle, Yipeng Zhou, Guanfeng Liu, Quan Z. Sheng, Cheng-Hsin Hsu
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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.
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Artificial Intelligence (cs.AI)
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arXiv:2609.15129 [cs.AI]
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arXiv:2609.15129v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.15129
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From: Yipeng Zhou [view email]
[v1]
Mon, 14 Sep 2026 07:05:05 UTC (1,714 KB)
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