ArticleCureus2024
Large-Language Models in Orthodontics: Assessing Reliability and Validity of ChatGPT in Pretreatment Patient Education.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
17 citing papers in PubMed.
- Evaluating the impact of AI-generated educational content on patient understanding and anxiety in endodontics and restorative dentistry: a comparative study.BMC oral health · 2025Trial
- Assessing the Diagnostic Performance of ChatGPT-5.0 versus Machine Learning in Orthodontics: A Comparative Analysis for Extraction Treatment Planning.Turkish journal of orthodontics · 2026Article
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Patient-Facing AI Chatbot Treatment-Direction Advice in Orthodontic Health Communication: A Scenario-Based Comparison with Expert Consensus.Healthcare (Basel, Switzerland) · 2026Article
- Prompt sensitivity of large language models in orthodontic patient counseling: a scenario-based experimental study.BMC oral health · 2026Article
- Review
- Aesthetic Component of the Index of Orthodontic Treatment Need (IOTN): Can large language models match the performance of orthodontists?Korean journal of orthodontics · 2026Article
- Generative Artificial Intelligence-driven orthodontic education practices.BMC medical education · 2026Review
- Comparative evaluation of ChatGPT and Gemini in detecting external apical root resorption on panoramic radiographs of orthodontic patients : Artificial intelligence performance in detecting root resorption.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 2025Article
- Orthodontic Biomechanical Reasoning with Multimodal Language Models: Performance and Clinical Utility.Bioengineering (Basel, Switzerland) · 2025Article
- Can Artificial Intelligence Language Models Effectively Address Dental Trauma Questions?Dental traumatology : official publication of International Association for Dental Traumatology · 2025Observational
- Comparative analysis of artificial intelligence chatbots in orthodontic emergency scenarios: ChatGPT-3.5, ChatGPT-4.0, Copilot, and Gemini.The Angle orthodontist · 2025Article
- Reliability of Large Language Model-Based Chatbots Versus Clinicians as Sources of Information on Orthodontics: A Comparative Analysis.Dentistry journal · 2025Article
- Comparative analysis of AI chatbot (ChatGPT-4.0 and Microsoft Copilot) and expert responses to common orthodontic questions: patient and orthodontist evaluations.BMC oral health · 2025Article
- Conversational artificial intelligence compared with orthodontic reference standards for orthodontic information: A systematic review of accuracy, readability, safety, and certainty of evidence.Journal of oral biology and craniofacial researchReview
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients seeking orthodontic treatment may use large language models (LLMs) such as Chat-GPT for self-education, thereby impacting their decision-making process. This study assesses the reliability and validity of Chat-GPT prompts aimed at informing patients about orthodontic side effects and examines patients' perceptions of this information. MATERIALS AND
methodsTo assess reliability, n = 28 individuals were asked to generate information from GPT-3.5 and Generative Pretrained Transformer 4 (GPT-4) about side effects related to orthodontic treatment using both self-formulated and standardized prompts. Three experts evaluated the content generated based on these prompts regarding its validity. We asked a cohort of 46 orthodontic patients about their perceptions after reading an AI-generated information text about orthodontic side effects and compared it with the standard text from the postgraduate orthodontic program at Aarhus University.
resultsAlthough the GPT-generated answers mentioned several relevant side effects, the replies were diverse. The experts rated the AI-generated content generally as "neither deficient nor satisfactory," with GPT-4 achieving higher scores than GPT-3.5. The patients perceived the GPT-generated information as more useful and more comprehensive and experienced less nervousness when reading the GPT-generated information. Nearly 80% of patients preferred the AI-generated information over the standard text.
conclusionsAlthough patients generally prefer AI-generated information regarding the side effects of orthodontic treatment, the tested prompts fall short of providing thoroughly satisfactory and high-quality education to patients.
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.