ArticleBDJ open2025
Assessing the power of AI: a comparative evaluation of large language models in generating patient education materials in dentistry.
Article in BDJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Large Language Models in Medical and Dental Education: A Cross-Sectional Comparison of AI-Generated and Faculty-Authored Prosthodontic Materials.Dentistry journal · 2026Article
- Comparative performance of large language models for patient-oriented support in dental trauma emergencies.BMC oral health · 2026Article
- Performance evaluation of large language models in bladder cancer patient education Q&A: a cross-sectional study.Frontiers in oncology · 2026Article
- Challenges of using generative AI for patient education in chronic heart failure: an evaluation of content quality, readability, and actionability in cross-platform LLM-generated texts.Frontiers in public health · 2026Article
- A centralized decision-making support consultation response for fertility preservation in breast cancer patients: benchmark performance of generative large language models in terms of reliability and readability.Frontiers in public health · 2026Article
- Performance of large language models in reporting oral health concerns and side effects in head and neck cancer: a comparative study.Journal of cancer research and clinical oncology · 2025Article
- The evaluation of tooth whitening from a perspective of artificial intelligence: a comparative analytical study.Frontiers in digital health · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study evaluates the use of large language models (LLMs) in generating Patient Education Materials (PEMs) for dental scenarios, focusing on their reliability, readability, understandability, and actionability. The study aimed to assess the performance of four LLMs-ChatGPT-4.0, Claude 3.5 Sonnet, Gemini 1.5 Flash, and Llama 3.1-405b-in generating PEMs for four common dental scenarios.
methodsA comparative analysis was conducted where five independent dental professionals assessed the materials using the Patient Education Materials Assessment Tool (PEMAT) to evaluate understandability and actionability. Readability was measured with Flesch Reading Ease and Level scores, and inter-rater reliability was assessed using Fleiss' Kappa.
resultsLlama 3.1-405b demonstrated the highest inter-rater reliability (Fleiss' Kappa: 0.78-0.89). ChatGPT-4.0 excelled in understandability, surpassing the PEMAT threshold of 70% in three of the four scenarios. Claude 3.5 Sonnet performed well in understandability for two scenarios but did not consistently meet the 70% threshold for actionability. ChatGPT-4.0 generated the longest responses, while Claude 3.5 Sonnet produced the shortest.
conclusionsChatGPT-4.0 demonstrated superior understandability, while Llama 3.1-405b achieved the highest inter-rater reliability. The findings indicate that further refinement and human intervention is necessary for LLM-generated content to meet the standards of effective patient education.
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Registered trials
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