ArticleBMC medical informatics and decision making2024
Can artificial intelligence models serve as patient information consultants in orthodontics?
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis.International dental journal · 2026Pooled it
- The role of large language models in dental diagnosis, decision-making, and communication: A systematic review.The Japanese dental science review · 2026Review
- Information Quality and Semantic Stability of AI Chatbot Responses to Complete Denture Questions: A Turkish-English Benchmarking Study.Healthcare (Basel, Switzerland) · 2026Article
- Patient-Facing AI Chatbot Treatment-Direction Advice in Orthodontic Health Communication: A Scenario-Based Comparison with Expert Consensus.Healthcare (Basel, Switzerland) · 2026Article
- Patient Facing AI Chatbots in Digital Orthodontic Education: A Comparative Evaluation of Understandability, Actionability, Quality, and Safety of Dietary Advice.Healthcare (Basel, Switzerland) · 2026Article
- Article
- Quality, completeness, readability, and response time of AI chatbots in the management of deep caries and pulp exposure: a bilingual comparative study.BMC oral health · 2026Article
- Prompt sensitivity of large language models in orthodontic patient counseling: a scenario-based experimental study.BMC oral health · 2026Article
- Reliability and readability of AI platforms for pediatric health advice: a comparative analysis.Scientific reports · 2026Article
- Accuracy, readability, and content coverage of AI-generated responses to questions on functional appliances.BMC oral health · 2026Article
- A bilingual evaluation of chatbot performance in bruxism-related information: accuracy and readability across two models.BMC oral health · 2026Article
- Evaluation of ChatGPT as a Source of Patient-Oriented Information on Gingival Recession.Healthcare (Basel, Switzerland) · 2026Article
- Evaluating the clinical safety of large language models in oral cancer-related patient communication: a repeated-prompt observational study.BMC oral health · 2026Observational
- Generative Artificial Intelligence-driven orthodontic education practices.BMC medical education · 2026Review
- Domain-Specific vs. General-Purpose Large Language Models in Orthodontics: A Blinded Comparison of AlimGPT, GPT-4o, Gemini, and Llama.Dentistry journal · 2026Article
- Exploring LLM-based chatbot effectiveness in answering questions related to the risks and benefits of orthognathic treatment: a cross-sectional study.European journal of orthodontics · 2026Article
- Accuracy, quality, and readability analyses of responses from large language models to questions on pediatric dental sedation.BMC oral health · 2026Article
- Orthodontic knowledge assessment: A comparison of five AI Chatbots.The Saudi dental journal · 2026Article
- AI-based decision support in pediatric dentistry: a comparative study of ChatGPT-5 and gemini advanced.BMC oral health · 2026Article
- Assessing the accuracy, reliability, quality, and readability of artificial intelligence chatbots in patient education: insights from zirconia crowns.BMC oral health · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo evaluate the accuracy, reliability, quality, and readability of responses generated by ChatGPT-3.5, ChatGPT-4, Gemini, and Copilot in relation to orthodontic clear aligners.
methodsFrequently asked questions by patients/laypersons about clear aligners on websites were identified using the Google search tool and these questions were posed to ChatGPT-3.5, ChatGPT-4, Gemini, and Copilot AI models. Responses were assessed using a five-point Likert scale for accuracy, the modified DISCERN scale for reliability, the Global Quality Scale (GQS) for quality, and the Flesch Reading Ease Score (FRES) for readability.
resultsChatGPT-4 responses had the highest mean Likert score (4.5 ± 0.61), followed by Copilot (4.35 ± 0.81), ChatGPT-3.5 (4.15 ± 0.75) and Gemini (4.1 ± 0.72). The difference between the Likert scores of the chatbot models was not statistically significant (p > 0.05). Copilot had a significantly higher modified DISCERN and GQS score compared to both Gemini, ChatGPT-4 and ChatGPT-3.5 (p < 0.05). Gemini's modified DISCERN and GQS score was statistically higher than ChatGPT-3.5 (p < 0.05). Gemini also had a significantly higher FRES compared to both ChatGPT-4, Copilot and ChatGPT-3.5 (p < 0.05). The mean FRES was 38.39 ± 11.56 for ChatGPT-3.5, 43.88 ± 10.13 for ChatGPT-4 and 41.72 ± 10.74 for Copilot, indicating that the responses were difficult to read according to the reading level. The mean FRES for Gemini is 54.12 ± 10.27, indicating that Gemini's responses are more readable than other chatbots.
conclusionsAll chatbot models provided generally accurate, moderate reliable and moderate to good quality answers to questions about the clear aligners. Furthermore, the readability of the responses was difficult. ChatGPT, Gemini and Copilot have significant potential as patient information tools in orthodontics, however, to be fully effective they need to be supplemented with more evidence-based information and improved readability.
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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.