ArticleJournal of orthopaedic case reports2025
Are Artificial Intelligence-generated Patient Leaflets Ready for Clinical Use? A Readability Comparison across Common Orthopaedic Procedures.
Article in Journal of orthopaedic case reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Evaluation and Comparison of Large Language Model Responses to Frequently Asked Questions Regarding Patellofemoral Pain Syndrome: A Quality and Readability Assessment Study.Healthcare (Basel, Switzerland) · 2026Article
- A comparative analysis of clinical quality and readability of neuropathic pain patient information leaflets: Türkiye versus the United States.Turkish journal of medical sciences · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Readable patient information is central to informed consent, shared decision-making, and treatment adherence. With the emergence of large language models (LLMs), such as ChatGPT, Gemini, and DeepSeek, there is growing interest in their role in generating health education content. However, the readability of such AI-generated patient information leaflets (PILs) has not been systematically compared with that of professionally authored materials. Objective: This study aimed to compare the readability of PILs generated by three generative artificial intelligence (AI) platforms with those produced by the Royal College of Surgeons of England (RCS England) for three common orthopedic procedures: Carpal tunnel release, total hip replacement, and total knee replacement. Materials and Methods: A total of 12 PILs (four per procedure) were analyzed using five validated readability metrics: Flesch reading ease, Flesch-Kincaid grade level, Gunning Fog Index, simple measure of Gobbledygook (SMOG) Index, and Coleman-Liau Index. Each AI model was prompted with a standardized instruction to generate a leaflet for the specified procedure. The RCS England leaflets served as the professional benchmark. Results: Across all metrics and procedures, RCS England leaflets demonstrated superior readability, with Flesch Reading Ease scores above 70 and Flesch-Kincaid Grade Levels between 5.52 and 7.15. In contrast, AI-generated leaflets frequently exceeded recommended complexity thresholds, with Grade Levels often above 12 and Gunning Fog and SMOG scores indicating post-secondary reading requirements. ChatGPT outputs were the most linguistically complex, while Gemini and DeepSeek produced intermediate but still suboptimal readability. Conclusion: While LLMs offer promising avenues for scalable health communication, current AI-generated PILs do not consistently meet recommended readability standards. Professionally authored leaflets remain more accessible for the average patient. These findings highlight the ongoing need for clinician oversight and quality assurance when integrating AI into patient education materials.
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