Performance of large language models in answering orthodontic questions: A scoping review.
5 affiliations
- 1Faculty of Dentistry, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam.
- 2School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
- 3School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
- 4Department of Dentistry, Taipei Medical University-Shuang Ho Hospital, Ministry of Health and Welfare, New Taipei City, Taiwan.
- 5Department of Dentistry, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
Abstract
Large language models (LLMs) are increasingly explored as novel platforms for delivering orthodontic information. Although their capacity to generate rapid, structured, and accessible responses has been widely recognized, concerns persist regarding performance variability and the risk of generating inaccurate outputs. Given the heterogeneity of existing evidence, this scoping review aimed to systematically map the current research landscape and synthesize findings on the application of LLMs in addressing orthodontic queries. This review was conducted in accordance with the PRISMA-ScR guidelines. Studies were retrieved from PubMed, Scopus, and the Cochrane Library up to November 30, 2025. Twenty-six studies met the eligibility criteria and were included in the analysis. Most adopted comparative designs evaluating different LLMs or model versions, with ChatGPT being the most frequently investigated system. Across studies, performance varied considerably depending on model version, topic domain, timing of inquiry, evaluator background, and assessment methodology. Generally, LLMs demonstrated moderate-to-high levels of scientific accuracy and acceptable informational quality. However, communication-related aspects revealed that responses were often difficult for lay audiences to comprehend. In conclusion, LLMs exhibit meaningful potential as adjunctive informational tools in orthodontics; however, their outputs cannot substitute professional consultation. Future research should focus on developing standardized evaluation frameworks, monitoring long-term performance, enhancing patient-centered readability, and establishing ethical and regulatory guidance to ensure the effective integration of LLMs into orthodontic information delivery.
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BibTeX
@article{nguyen2026performance,
title = {{Performance of large language models in answering orthodontic questions: A scoping review.}},
author = {Tu Manh Nguyen and Fang-Yu Fan and Ying-Sui Sun and Yang-Che Wu and Sheng-Wei Feng and Hao-Ting Chen and I-Ta Lee and Pao-Chang Chiang},
journal = {Journal of dental sciences},
year = {2026},
month = oct,
doi = {10.1016/j.jds.2025.07.3447},
url = {https://europepmc.org/article/MED/42924142},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Performance of large language models in answering orthodontic questions: A scoping review.
AU - Tu Manh Nguyen
AU - Fang-Yu Fan
AU - Ying-Sui Sun
AU - Yang-Che Wu
AU - Sheng-Wei Feng
AU - Hao-Ting Chen
AU - I-Ta Lee
AU - Pao-Chang Chiang
PY - 2026
DA - 2026/10/01
JO - Journal of dental sciences
DO - 10.1016/j.jds.2025.07.3447
UR - https://europepmc.org/article/MED/42924142
AB - Large language models (LLMs) are increasingly explored as novel platforms for delivering orthodontic information. Although their capacity to generate rapid, structured, and accessible responses has been widely recognized, concerns persist regarding performance variability and the risk of generating inaccurate outputs. Given the heterogeneity of existing evidence, this scoping review aimed to systematically map the current research landscape and synthesize findings on the application of LLMs in addressing orthodontic queries. This review was conducted in accordance with the PRISMA-ScR guidelines. Studies were retrieved from PubMed, Scopus, and the Cochrane Library up to November 30, 2025. Twenty-six studies met the eligibility criteria and were included in the analysis. Most adopted comparative designs evaluating different LLMs or model versions, with ChatGPT being the most frequently investigated system. Across studies, performance varied considerably depending on model version, topic domain, timing of inquiry, evaluator background, and assessment methodology. Generally, LLMs demonstrated moderate-to-high levels of scientific accuracy and acceptable informational quality. However, communication-related aspects revealed that responses were often difficult for lay audiences to comprehend. In conclusion, LLMs exhibit meaningful potential as adjunctive informational tools in orthodontics; however, their outputs cannot substitute professional consultation. Future research should focus on developing standardized evaluation frameworks, monitoring long-term performance, enhancing patient-centered readability, and establishing ethical and regulatory guidance to ensure the effective integration of LLMs into orthodontic information delivery.
SN - 1991-7902
SN - 2213-8862
ER -