ArticleFrontiers in public health2026
Evaluation of validity, reliability, and readability of AI chatbots for gestational diabetes mellitus: a multi-model comparative study.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Comparative Evaluation of AI Chatbots for Testicular Cancer Education: Validity, Information Quality, and Readability.Annals of surgical oncology · 2026Article
- Evaluation of Five Large Language Models for Parental Education in Pediatric Anesthesia: Reliability and Readability Study.JMIR medical informatics · 2026Article
- Benchmarking publicly accessible large language models for English-language patient-facing acute pancreatitis information: a cross-sectional study of quality, transparency, and readability.Frontiers in public health · 2026Article
- Evaluation of large language model-generated information in diabetes health patient education: a scoping review.Frontiers in public health · 2026Article
- Assessing the information quality of AI-generated patient educational materials for diabetes: a scoping review.Frontiers in public health · 2026Article
- Evaluating large language models for myocardial infarction public health education: a comparative study on information quality, transparency and readability.Frontiers in public health · 2026Article
- Evaluating the accuracy, reliability, and readability of AI chatbots in delivering postpartum depression information.Frontiers in psychiatry · 2026Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Gestational 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. Methods: We 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. Results: Overall MCQ accuracy differed significantly across the six chatbots ( Conclusion: 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.
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