ArticleScientific reports2025
Evaluating the readability and quality of AI-generated scoliosis education materials: a comparative analysis of five language models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Research priorities in Spine Deformity: a machine learning-based topic analysis of the journal's first decade (2013-2026).Spine deformity · 2026Article
- From Algorithms to Clinics: Recent Progress in AI for Scoliosis Diagnosis and Management.Journal of clinical medicine · 2026Review
- Performance of three large language models in answering parent-focused questions on rickets: a dual pediatric-orthopedic specialist evaluation.BMC pediatrics · 2026Article
- ChatGPT-5.4 in health education: inter-generation stability and persistent readability challenges.Frontiers in public health · 2026Article
- Evaluating large language models for ADHD education: A comparative study of ChatGPT 5, DeepSeek V3, and Grok 4.PloS one · 2026Article
- Evaluating large language models as tools for public health education on scoliosis.Frontiers in public health · 2026Article
- Benchmarking the readability, quality, and educational suitability of large language models in communicating pertussis.Frontiers in public health · 2026Article
- Comparative performance of ChatGPT and DeepSeek in interpreting the 2025 ESICM guidelines on sepsis fluid therapy.Digital healthArticle
- Exploring evaluation measures of large language models for family caregiver use: A scoping review.Digital healthReview
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The complexity of scoliosis-related terminology and treatment options often hinders patients and caregivers from understanding their choices, making it difficult to make informed decisions. As a result, many patients seek guidance from artificial intelligence (AI) tools. However, AI-generated health content may suffer from low readability, inconsistency, and questionable quality, posing risks of misinformation. This study evaluates the readability and informational quality of scoliosis-related content produced by AI. We evaluated five AI models-ChatGPT-4o, ChatGPT-o1, ChatGPT-o3 mini-high, DeepSeek-V3, and DeepSeek-R1-by querying each on three types of scoliosis: congenital, adolescent idiopathic, and neuromuscular. Readability was assessed using the Flesch-Kincaid Grade Level (FKGL) and FleschKincaid Reading Ease (FKRE), while content quality was evaluated using the DISCERN score. Statistical analyses were performed in R-Studio. Inter-rater reliability was calculated using the Intraclass Correlation Coefficient (ICC). DeepSeek-R1 achieved the lowest FKGL (6.2) and the highest FKRE (64.5), indicating superior readability. In contrast, ChatGPT-o1 and ChatGPT-o3 mini-high scored above FKGL 12.0, requiring college-level reading skills. Despite readability differences, DISCERN scores remained stable across models (~ 50.5/80) with high inter-rater agreement (ICC = 0.85-0.87), suggesting a fair level quality. However, all responses lacked citations, limiting reliability. AI-generated scoliosis education materials vary significantly in readability, with DeepSeek-R1 being the most accessible. Future AI models should enhance readability without compromising information accuracy and integrate real-time citation mechanisms for improved trustworthiness.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.