ArticleBMC oral health2025
Comparing orthodontic pre-treatment information provided by large language models.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- 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
- Prompt sensitivity of large language models in orthodontic patient counseling: a scenario-based experimental study.BMC oral health · 2026Article
- Generative Artificial Intelligence-driven orthodontic education practices.BMC medical education · 2026Review
- Optimizing Large Language Model Responses to Medical Queries: a Cross-sectional Study On the Effective Use of Chatgpt for Cancer-related Questions.Journal of medical systems · 2026Article
- Navigation, Adoption, and Use of Digital Health Technologies for Irritable Bowel Syndrome Self-Management: Focus Group Study of Patient Experience and Decision-Making.JMIR human factors · 2026Article
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study collected and screened the 50 most common pre-treatment consultation questions from adult orthodontic patients through clinical practice. Responses to these questions were generated using three large language models: Ernie Bot, ChatGPT, and Gemini. The responses were evaluated across six dimensions: Professional Accuracy (PA), Accuracy of Content(AC), Clarity and Comprehensibility (CC), Personalization and Relevance (PR), Information Completeness (IC), and Empathy and Patient-Centeredness (EHC). Results indicated that scores for each group in various dimensions primarily fell within the range of 3-4 points, with relatively few high-quality scores (5 points). While large language models demonstrate some capability in addressing open-ended questions, their use in medical consultation, particularly in orthodontic medicine, requires caution and further integration with professional guidance and verification. Future research and technological improvements should focus on enhancing AI(Artificial Intelligence) performance in accuracy, information completeness, and humanistic care to better meet the needs of diverse clinical scenarios.
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.