ArticleThe Angle orthodontist2024
How reliable is the artificial intelligence product large language model ChatGPT in orthodontics?
Article in The Angle orthodontist, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Educational Applications of ChatGPT in University-Based Dental Education. A Systematic Review.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Pooled it
- Can Large Language Models Serve as Reliable Tools for Information in Dentistry? A Systematic Review.International dental journal · 2025Pooled it
- 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
- Role-based evaluation of artificial intelligence chatbot responses in orthodontic emergency scenarios: accuracy, readability, understandability, and patient-oriented communication.BMC oral health · 2026Article
- Review
- Accuracy, readability, and content coverage of AI-generated responses to questions on functional appliances.BMC oral health · 2026Article
- Evaluating large language models for orthodontic consultation in patients with periodontitis: a study of reliability, quality, and readability.BMC oral health · 2026Article
- Generative Artificial Intelligence-driven orthodontic education practices.BMC medical education · 2026Review
- 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
- Orthodontic knowledge assessment: A comparison of five AI Chatbots.The Saudi dental journal · 2026Article
- Comparative evaluation of ChatGPT, Gemini, and Grok in clinical decision-making and general knowledge assessment for impacted maxillary canines.Korean journal of orthodontics · 2026Article
- What artificial intelligence (AI) can tell us about Nasoalveolar Molding (NAM)?BMC oral health · 2025Article
- Beyond virality: evaluating evidence-based content delivery in social media-based pediatric orthodontic education.BMC oral health · 2025Article
- Classification Performance of Deep Learning Models for the Assessment of Vertical Dimension on Lateral Cephalometric Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Can AI chatbots accurately provide information on orthodontic risks?The Angle orthodontist · 2025Article
- Integrating Artificial Intelligence into Orthodontic Education: A Systematic Review and Meta-Analysis of Clinical Teaching Application.Journal of clinical medicine · 2025Review
- A multi-dimensional performance evaluation of large language models in dental implantology: comparison of ChatGPT, DeepSeek, Grok, Gemini and Qwen across diverse clinical scenarios.BMC oral health · 2025Article
- Comparing orthodontic pre-treatment information provided by large language models.BMC oral health · 2025Article
- Generative Pre-trained Transformer: Trends, Applications, Strengths and Challenges in Dentistry: A Systematic Review.Healthcare informatics research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate the reliability of information produced by the artificial intelligence-based program ChatGPT in terms of accuracy and relevance, as assessed by orthodontists, dental students, and individuals seeking orthodontic treatment. MATERIALS AND
methodsFrequently asked and curious questions in four basic areas related to orthodontics were prepared and asked in ChatGPT (Version 4.0), and answers were evaluated by three different groups (senior dental students, individuals seeking orthodontic treatment, orthodontists). Questions asked in these basic areas of orthodontics were about: clear aligners (CA), lingual orthodontics (LO), esthetic braces (EB), and temporomandibular disorders (TMD). The answers were evaluated by the Global Quality Scale (GQS) and Quality Criteria for Consumer Health Information (DISCERN) scale.
resultsThe total mean DISCERN score for answers on CA for students was 51.7 ± 9.38, for patients was 57.2 ± 10.73 and, for orthodontists was 47.4 ± 4.78 (P = .001). Comparison of GQS scores for LO among groups: students (3.53 ± 0.78), patients (4.40 ± 0.72), and orthodontists (3.63 ± 0.72) (P < .001). Intergroup comparison of ChatGPT evaluations about TMD was examined in terms of the DISCERN scale, with the highest value given in the patients group (57.83 ± 11.47) and lowest value in the orthodontist group (45.90 ± 11.84). When information quality evaluation about EB was examined, it GQS scores were >3 in all three groups (students: 3.50 ± 0.78; patients: 4.17 ± 0.87; orthodontists: 3.50 ± 0.82).
conclusionsChatGPT has significant potential in terms of usability for patient information and education in the field of orthodontics if it is developed and necessary updates are made.
Indexed as
Identifiers
What OpenQuestion holds
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.