To evaluate the accuracy, reliability, quality, and readability of responses generated by ChatGPT-3.5, ChatGPT-4, Gemini, and Copilot in relation to orthodontic clear aligners. Frequently asked questions by patients/laypersons about clear aligners on websites were identified using the Google search tool and these questions were posed to ChatGPT-3.5, ChatGPT-4, Gemini, and Copilot AI models. Responses were assessed using a five-point Likert scale for accuracy, the modified DISCERN scale for reliability, the Global Quality Scale (GQS) for quality, and the Flesch Reading Ease Score (FRES) for readability. ChatGPT-4 responses had the highest mean Likert score (4.5 ± 0.61), followed by Copilot (4.35 ± 0.81), ChatGPT-3.5 (4.15 ± 0.75) and Gemini (4.1 ± 0.72). The difference between the Likert scores of the chatbot models was not statistically significant (p > 0.05). Copilot had a significantly higher modified DISCERN and GQS score compared to both Gemini, ChatGPT-4 and ChatGPT-3.5 (p < 0.05). Gemini’s modified DISCERN and GQS score was statistically higher than ChatGPT-3.5 (p < 0.05). Gemini also had a significantly higher FRES compared to both ChatGPT-4, Copilot and ChatGPT-3.5 (p < 0.05). The mean FRES was 38.39 ± 11.56 for ChatGPT-3.5, 43.88 ± 10.13 for ChatGPT-4 and 41.72 ± 10.74 for Copilot, indicating that the responses were difficult to read according to the reading level. The mean FRES for Gemini is 54.12 ± 10.27, indicating that Gemini’s responses are more readable than other chatbots. All chatbot models provided generally accurate, moderate reliable and moderate to good quality answers to questions about the clear aligners. Furthermore, the readability of the responses was difficult. ChatGPT, Gemini and Copilot have significant potential as patient information tools in orthodontics, however, to be fully effective they need to be supplemented with more evidence-based information and improved readability.

This study evaluated the performance of a multimodal large language model (MLLM), Chat Generative Pretrained Transformer-5.5 (ChatGPT-5.5), in determining orthodontic treatment need based on the Index of Complexity, Outcome, and Need (ICON) using intraoral photographs and digital model images. Agreement at the ICON component level and consistency with the total ICON score were also examined. A total of 104 patients contributed 520 intraoral photographs and 520 rendered digital model images. Two orthodontists independently scored all ICON components (aesthetics, crowding/spacing, crossbite, vertical relationship, and buccal relationship), and cases with complete interrater agreement were used as the reference standard. A standardized ICON prompt for ChatGPT-5.5 was optimized using the Iterative Prompt Calibration (IPC) method. Model outputs were assessed using accuracy, sensitivity, specificity, F1-score, exact agreement rates, Cohen's kappa, and intraclass correlation coefficients (ICC). Confusion matrices and ROC curves were generated for the binary treatment-need decision. The ChatGPT-5.5 achieved overall accuracies of 74.0% for intraoral photographs and 72.1% for model images. Recall was high for treatment-required cases (0.871 and 1.00, respectively) but markedly lower for cases not requiring treatment (0.471 and 0.147). Component-level agreement varied considerably, with significant differences between photographs and models for crowding/spacing and vertical relationships. The anteroposterior relationship showed higher accuracy in model evaluations. ICC values for the total ICON score were 0.463 for photographs (moderate reliability) and 0.154 for models (poor reliability). AUC values were 0.671 and 0.574, indicating limited discriminative performance. Although ChatGPT-5.5, evaluated as a MLLM, demonstrated moderate accuracy in the binary classification of orthodontic treatment need, its inconsistent performance across ICON components and tendency to overestimate treatment need limit its current clinical reliability. Therefore, clinical decision-making should remain guided by expert judgment.

Background: Dental age (DA) estimation, which is widely used in orthodontics, pediatric dentistry, and forensic dentistry, predicts chronological age (CA) by assessing tooth development and maturation. Most methods rely on radiographic evaluation of tooth mineralization and eruption stages to assess DA. With the increasing adoption of large language models (LLMs) in medical sciences, use of ChatGPT has extended to processing visual data. The aim of this study, therefore, was to evaluate the performance of ChatGPT-4 in estimating DA from panoramic radiographs using three conventional methods (Nolla, Haavikko, and London Atlas) and to compare its accuracy against both orthodontist assessments and CA. Methods: In this retrospective study, panoramic radiographs of 511 Turkish children aged 6–17 years were assessed. DA was estimated using the Nolla, Haavikko, and London Atlas methods by both orthodontists and ChatGPT-4. The DA–CA difference and mean absolute error (MAE) were calculated, and statistical comparisons were performed to assess accuracy and sex differences and reach an agreement between the evaluators, with significance set at p < 0.05. Results: The mean CA of the study population was 12.37 ± 2.95 years (boys: 12.39 ± 2.94; girls: 12.35 ± 2.96). Using the London Atlas method, the orthodontists overestimated CA with a DA–CA difference of 0.78 ± 1.26 years (p < 0.001), whereas ChatGPT-4 showed no significant DA–CA difference (0.03 ± 0.93; p = 0.399). Using the Nolla method, the orthodontist showed no significant DA–CA difference (0.03 ± 1.14; p = 0.606), but ChatGPT-4 underestimated CA with a DA–CA difference of −0.40 ± 1.96 years (p < 0.001). Using the Haavikko method, the evaluators underestimated CA (orthodontist: −0.88; ChatGPT-4: −1.18; p < 0.001). The lowest MAE for ChatGPT-4 was obtained when using the London Atlas method (0.59 ± 0.72), followed by Nolla (1.33 ± 1.28) and Haavikko (1.51 ± 1.41). For the orthodontists, the lowest MAE was achieved when using the Nolla method (0.86 ± 0.75). Agreement between the orthodontists and ChatGPT-4 was highest when using the London Atlas method (ICC = 0.944, r = 0.905). Conclusions: ChatGPT-4 showed the highest accuracy with the London Atlas method, with no significant difference from CA for either sex or the lowest prediction error. When using the Nolla and Haavikko methods, both ChatGPT-4 and the orthodontist tended to underestimate age, with higher errors. Overall, ChatGPT-4 performed best when using visually guided methods and was less accurate when using multi-stage scoring methods.

Digital technologies have become increasingly integrated into orthodontic practice for diagnosis, treatment planning, and appliance manufacturing. This study aimed to assess undergraduate dental students’ awareness and attitudes toward the use of digital technologies in orthodontics and to explore the potential influence of academic year and intended specialization on these perceptions. This cross-sectional study was conducted among third-, fourth-, and fifth-year undergraduate students at Istanbul Aydin University, Faculty of Dentistry, during the 2023–2024 academic year. A structured online questionnaire was developed to evaluate students’ awareness and attitudes regarding the use of digital technologies in orthodontics. The questionnaire comprised three sections: demographic information, binary (yes/no) questions assessing awareness, and seven attitude statements evaluated on a 5-point Likert scale. Group comparisons were performed using the Pearson Chi-square test or the Fisher–Freeman–Halton exact test for categorical variables, and the Kruskal–Wallis test for ordinal data. Explanatory factor analysis using principal component analysis with Varimax rotation was applied to assess the construct validity of the attitude scale. Statistical significance was set at p < 0.05. A total of 278 dental students participated in the study, including 179 females (64.4%) and 99 males (35.6%), with representation from the third (37.4%), fourth (34.9%), and fifth (27.7%) academic years. The most commonly recognized digital orthodontic applications were the fabrication of clear aligners using three-dimensional (3D) printing (91.7%) and the use of intraoral scanners for digital impressions (87.1%). Awareness of cone-beam computed tomography (CBCT) increased significantly with academic year (third: 36.5%, fifth: 71.4%, p < 0.05), while awareness of digital treatment planning tools declined (third: 43.3%, fifth: 24.7%, p = 0.025). Similarly, knowledge of 3D-printed appliances (third: 43.3%, fourth: 15.5%, p < 0.05) and in-office aligner production (third: 36.5%, fourth: 67%, p < 0.05) varied significantly across years. Attitudinal responses indicated generally positive views toward the integration of digital technologies into orthodontic practice and education. Significant differences were also observed in selected attitude items according to academic year and intended specialization (p < 0.05). This study demonstrated that dental students were more familiar with commonly used technologies such as intraoral scanners and 3D-printed clear aligners, whereas their awareness of other digital applications in orthodontics remained limited. Overall, attitudes toward digital integration were positive across all academic years. Interestingly, despite having more clinical experience, final-year students reported lower awareness in certain areas, underscoring the importance of complementing early theoretical education with consistent and structured clinical exposure in later years. Not applicable.

Aim: The aim of this study was to evaluate patients' attitudes and perspectives on the relationship between fixed orthodontic treatment and oral hygiene. Material and methods: The study included 175 patients undergoing fixed orthodontic treatment. Data was collected through questionnaires that included patient demographics, oral hygiene behaviours, perspectives on white spot lesions (WSL) and periodontal problems, and attitudes towards orthodontic treatment. The chi-square test was used to analyse the relationship between categorical data. The significance level was set at p<0.05. Results: The mean age of the patients (109 males, 66 females) was 17.8±6.4 years. Most of the patients who received orthodontic treatment brushed their teeth twice a day. Traditional and orthodontic toothbrushes were preferred over electric ones. The most common oral hygiene tools were toothbrushes and interdental brushes. A total of 151 patients (86.28%) indicated that their primary source of oral hygiene guidance was the orthodontist. A total of 113 patients (64.57%) had no knowledge of dental plaque, while 117 patients (66.85%) had no knowledge of WSL. While 80 patients (84%) with knowledge about periodontal problems believed that the primary cause of periodontal problems was inadequate oral hygiene, 43 patients (74.85%) with knowledge about WSL attributed the main cause of WSL to the use of braces. Negative attitudes towards orthodontic treatment and oral hygiene were more prevalent among adolescents (p<0.05), with no difference in gender or treatment duration. Patients who received information about WSL and periodontal problems exhibited a more positive attitude towards treatment and oral hygiene (p<0.05). Conclusion: This study highlights the limited patient awareness of plaque and WSL during fixed orthodontic treatment. Physician guidance has a significant impact on patient attitudes, and specific educational programmes are required in orthodontic treatment.