Comparison of artificial intelligence-generated and physician-generated patient education materials on early diabetic kidney disease

作者：Miaomiao Cheng, Qi Zhang, Hua Liang, Yanan Wang, Jun Qin, Lei Gong, Sha Wang, Luyao Li, Xiaoyan Xiao · 发表于：Frontiers in Endocrinology · 年份：2025 · DOI：10.3389/fendo.2025.1559265 · 被引用次数：15 · 研究领域：Artificial Intelligence in Healthcare and Education、Mobile Health and mHealth Applications、Machine Learning in Healthcare

Background: Diabetic kidney disease (DKD) is a common and serious complication of diabetes mellitus and has become the most important cause of end-stage renal disease (ESRD). In light of the rising prevalence of diabetes, there is a growing imperative for the early detection and intervention of DKD. With the rapid development of artificial intelligence (AI) technologies, its potential applications in patient education are receiving increasing attention, especially large language models (LLMs). The aim of this study was to evaluate the quality of LLMs-generated patient education materials (PEMs) for early DKD and to explore its feasibility in patient education. Methods: Four LLMs (ERNIE Bot 4.0, GPT-4o, ChatGLM4, and ChatGPT-o1) were selected for this study to generate PEMs. Among them, ERNIE Bot 4.0, GPT-4o, and ChatGLM4 generated 2 versions of PEMs based on American Diabetes Association(ADA) guidelines and without ADA guidelines, respectively. ChatGPT-o1 only generated a PEM without ADA guidelines. An experienced physician wrote a PEM based on ADA guidelines. All materials were assessed using a Likert scale which covered the dimensions of accuracy, completeness, safety, and patient comprehensibility. A total of 7 medical experts (including nephrologists and endocrinologists) and 50 diabetic patients were invited to evaluate the study. We recorded basic information on the patient evaluators. Results: Experts evaluated PEMs from ERNIE Bot 4.0, GPT-4o, ChatGLM4, and ChatGPT-o1,...