ReviewJournal of oral biology and craniofacial research
Conversational artificial intelligence compared with orthodontic reference standards for orthodontic information: A systematic review of accuracy, readability, safety, and certainty of evidence.
Review in Journal of oral biology and craniofacial research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Conversational artificial intelligence (AI) is increasingly used to obtain orthodontic information, but its reliability, completeness, readability, agreement with specialist judgment, and safety remain uncertain. Objective: To systematically evaluate AI chatbots for delivering orthodontic information and clinical guidance, focusing on accuracy/reliability, completeness, readability, specialist agreement, and misleading information. Methods: This systematic review followed PRISMA 2020 and Cochrane guidance. Embase, PubMed, Scopus, Web of Science, ClinicalTrials.gov, Open Science Framework, and Google Scholar were searched. Eligible studies assessed AI-generated orthodontic responses against specialists, reference answers, or structured assessment tools. Risk of bias was evaluated using the Joanna Briggs Institute checklist. Meta-analysis was not performed because of substantial heterogeneity in chatbot systems, prompts, comparators, scoring instruments, and outcome definitions. Certainty of evidence was assessed using GRADE. Results: Sixteen analytical cross-sectional studies were included. Chatbots generated coherent and accessible answers to routine orthodontic questions, especially foundational concepts and patient frequently asked questions. However, performance varied by model version, prompt design, and clinical complexity. Important limitations were identified in treatment planning, individualized recommendations, contextual risk framing, and information completeness. Some studies reported incomplete or false claims regarding clear aligners, airway effects, stability, and surgery. Readability was generally favorable but did not ensure clinical adequacy. Risk of bias ranged from low to moderate, and certainty of evidence was very low. Conclusions: AI chatbots may support routine orthodontic education under professional supervision, but current evidence does not support autonomous use. Standardized prompts, validated scoring tools, calibrated evaluators, and real-world outcome studies are needed.
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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.