妊娠期糖尿病AI聊天机器人有效性、可靠性和可读性评估比较研究（表1）
评估六种AI聊天机器人在妊娠期糖尿病问答中的准确性、可靠性和可读性的比较研究数据（表格形式）。
基本信息
资源简介
妊娠期糖尿病AI聊天机器人有效性、可靠性和可读性评估比较研究（表1）
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 16.09 KB，主题涵盖 artificial intelligence、gestational diabetes mellitus、large language models、patient education、readability，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Table 1_Evaluation of validity, reliability, and readability of AI chatbots for gestational diabetes mellitus: a multi-model comparative study.docx》。配套论文《IDF Diabetes Atlas: Estimation of Global and Regional Gestational Diabetes Mellitus Prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group’s Criteria》发表于 Diabetes Research and Clinical Practice（2022），DOI：10.1016/j.diabres.2021.109050。源站首次发布：2026-02-04。
【数据内容】
源站原始描述：BackgroundGestational diabetes mellitus (GDM) is increasingly prevalent worldwide and is associated with substantial short- and long-term risks for mothers and offspring, making high-quality, accessible health information essential. At the same time, artificial intelligence (AI) chatbots based on large language models are being widely used for health queries, yet their accuracy, reliability and readability in the context of GDM remain unclear. MethodsWe first evaluated six AI chatbots (ChatGPT-5…
中文译文（机器翻译，仅供快速理解，请以原文为准）：背景妊娠期糖尿病（GDM）在全球范围内日益普遍，并给母亲和子代带来显著的短期和长期风险，因此高质量、可获取的健康信息至关重要。与此同时，基于大语言模型的人工智能（AI）聊天机器人正被广泛用于健康咨询，但其在GDM背景下的准确性、可靠性和可读性仍不明确。 方法我们首先使用200道关于GDM的单选最佳答案多项选择题（MCQ）评估了六种AI聊天机器人（ChatGPT-5、ChatGPT-4o、DeepSeek-V3.2、DeepSeek-R1、Gemini 2.5 Pro和Claude Sonnet 4.5），这些题目取自MedQA、MedMCQA和中国国家医学考试题库，涵盖四个领域：流行病学与危险因素、临床表现与诊断、母婴结局以及管理与治疗。在标准化提示方案下，每道题均向每个模型提问三次，准确性定义为正确回答题目所占比例。对于面向公众的信息，我们使用Google Trends和专家评审确定了15个GDM核心教育问题，并查询了四种聊天机器人（ChatGPT-5、DeepSeek-V3.2、Claude Sonnet 4.5和Gemini 2.5 Pro）。两名产科医生使用DISCERN、EQIP、GQS和JAMA基准独立评估可靠性，并使用ARI、CL、FKGL、FRES、GFI和SMOG指数对可读性进行量化。 结果六种聊天机器人的总体MCQ准确性存在显著差异（p 当代AI聊天机器人能够生成总体准确且可靠性中等的GDM相关信息，较新的模型代际在诊断有效性方面显示出明显提升。然而，透明度有限以及阅读水平系统性偏高表明，这些工具尚不适合作为GDM患者教育的独立资源，应作为临床医生咨询和专业策划材料的辅助手段使用。
【数据结构与技术规格】
文件数 1 个，合计 16.09 KB。Table 1_Evaluation of validity, reliability, and readability of AI chatbots for gestational diabetes mellitus: a multi-model comparative study.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，16.09 KB。数据集 DOI：10.3389/fpubh.2026.1760871.s001。
【主题与分类】
源站学科分类：Public Health and Health Services not elsewhere classified。主题标签：artificial intelligence、gestational diabetes mellitus、large language models、patient education、readability。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Evaluation_of_validity_reliability_and_readability_of_AI_chatbots_for_gestational_diabetes_mellitus_a_multi-model_comparative_study_docx/31248007
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Wang, Xinxin; Lin, Shuyan; Liu, Hui; Li, Chuanqing; Zhou, Li; Li, Rongkang (2026). Table 1_Evaluation of validity, reliability, and readability of AI chatbots for gestational diabetes mellitus: a multi-model comparative study.docx. Frontiers. Dataset
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使用方式
数据集获取（Figshare）
- 源站标题：Table 1_Evaluation of validity, reliability, and readability of AI chatbots for gestational diabetes mellitus: a multi-model comparative study.docx
- DOI：10.3389/fpubh.2026.1760871.s001
- 发布日期：2026-02-04
- 文件数：1 个
- 文件总体积：16.09 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Evaluation_of_validity_reliability_and_readability_of_AI_chatbots_for_gestational_diabetes_mellitus_a_multi-model_comparative_study_docx/31248007
命令行下载
curl -L -o "Table 1_Evaluation of validity, reliability, and readability of AI chatbots for gestational diabetes mellitus: a multi-model comparative study.docx" "https://ndownloader.figshare.com/files/61626988"
文件清单
Table 1_Evaluation of validity, reliability, and readability of AI chatbots for gestational diabetes mellitus: a multi-model comparative study.docx（16.09 KB）
文件总体积 16.09 KB（源站 API 实测）。
数据说明
BackgroundGestational diabetes mellitus (GDM) is increasingly prevalent worldwide and is associated with substantial short- and long-term risks for mothers and offspring, making high-quality, accessible health information essential. At the same time, artificial intelligence (AI) chatbots based on large language models are being widely used for health queries, yet their accuracy, reliability and readability in the context of GDM remain unclear.
MethodsWe first evaluated six AI chatbots (ChatGPT-5, ChatGPT-4o, DeepSeek-V3.2, DeepSeek-R1, Gemini 2.5 Pro and Claude Sonnet 4.5) using 200 single-best-answer multiple-choice questions (MCQs) on GDM drawn from MedQA, MedMCQA and the Chinese National Medical Examination item bank, covering four domains: epidemiology and risk factors, clinical manifestations and diagnosis, maternal and neonatal outcomes, and management and treatment. Each item was posed three times to every model under a standardized prompting protocol, and accuracy was defined as the proportion of correctly answered questions. For public-facing information, we identified 15 core GDM education questions using Google Trends and expert review, and queried four chatbots (ChatGPT-5, DeepSeek-V3.2, Claude Sonnet 4.5 and Gemini 2.5 Pro). Two obstetricians independently assessed reliability using DISCERN, EQIP, GQS and JAMA benchmarks, and readability was quantified using ARI, CL, FKGL, FRES, GFI and SMOG indices.
ResultsOverall MCQ accuracy differed significantly across the six chatbots (p Contemporary AI chatbots can generate generally accurate and moderately reliable GDM-related information, with newer model generations showing clear gains in diagnostic validity. However, limited transparency and systematically high reading levels indicate that these tools are not yet suitable as stand-alone resources for GDM patient education and should be used as adjuncts to clinician counseling and professionally curated materials.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fpubh.2026.1760871.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Evaluation_of_validity_reliability_and_readability_of_AI_chatbots_for_gestational_diabetes_mellitus_a_multi-model_comparative_study_docx/31248007）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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