Summary: Background: The increasing utilization of large language models (LLMs) in Generative Artificial Intelligence across various medical and dental fields, and specifically orthodontics, raises questions about their accuracy. Objective: This study aimed to assess and compare the answers offered by four LLMs: Google’s Bard, OpenAI’s ChatGPT-3.5, and ChatGPT-4, and Microsoft’s Bing, in response to clinically relevant questions within the field of orthodontics. Materials and methods: Ten open-type clinical orthodontics-related questions were posed to the LLMs. The responses provided by the LLMs were assessed on a scale ranging from 0 (minimum) to 10 (maximum) points, benchmarked against robust scientific evidence, including consensus statements and systematic reviews, using a predefined rubric. After a 4-week interval from the initial evaluation, the answers were reevaluated to gauge intra-evaluator reliability. Statistical comparisons were conducted on the scores using Friedman’s and Wilcoxon’s tests to identify the model providing the answers with the most comprehensiveness, scientific accuracy, clarity, and relevance. Results: Overall, no statistically significant differences between the scores given by the two evaluators, on both scoring occasions, were detected, so an average score for every LLM was computed. The LLM answers scoring the highest, were those of Microsoft Bing Chat (average score = 7.1), followed by ChatGPT 4 (average score = 4.7), Google Bard (average score = 4.6), and finally ChatGPT 3.5 (average score 3.8). While Microsoft Bing Chat statistically outperformed ChatGPT-3.5 (P-value = 0.017) and Google Bard (P-value = 0.029), as well, and Chat GPT-4 outperformed Chat GPT-3.5 (P-value = 0.011), all models occasionally produced answers with a lack of comprehensiveness, scientific accuracy, clarity, and relevance. Limitations: The questions asked were indicative and did not cover the entire field of orthodontics. Conclusions: Language models (LLMs) show great potential in supporting evidence-based orthodontics. However, their current limitations pose a potential risk of making incorrect healthcare decisions if utilized without careful consideration. Consequently, these tools cannot serve as a substitute for the orthodontist’s essential critical thinking and comprehensive subject knowledge. For effective integration into practice, further research, clinical validation, and enhancements to the models are essential. Clinicians must be mindful of the limitations of LLMs, as their imprudent utilization could have adverse effects on patient care.

Background: The use of digital technology has exponentially increased over recent years. Intraoral scanners, especially, have gained traction within orthodontics. The objective of the present review is to investigate the available evidence to create an up-to-date presentation of various clinical aspects of intraoral scanners in orthodontics. Methods: Search without restrictions in seven databases (Pubmed, CENTRAL, Cochrane Reviews, Scopus, Web of Science, Clinical Trials, Proquest) since inception, and hand searching until October 2020, were conducted. Results: The majority of studies were either cross-over or parallel group studies. The accuracy and reproducibility of intraoral scanners, in comparison to conventional methods, were investigated in several studies, with controversial results. The duration of the procedure did not report any clear outcome in favor of any method. Patients seem to prefer intraoral scanning, even though numerous studies point out the importance of operators’ experience and skills. Conclusions: Despite the innovations that intraoral scanners have brought in orthodontic clinical practice, there are still some challenges and limitations in their use. The majority of existing limitations may be overcome with experience and good clinical skills. More high-quality studies need to be conducted so that clinicians can have a clear image of this new technology.

Background: Orthodontic clear aligners constitute an alternative and increasingly used orthodontic treatment modality, offering enhanced esthetics with potential consequences regarding patients’ oral health-related quality of life (OHRQoL). Objective: Summarize and systematically evaluate existing evidence on the OHRQoL of patients treated with orthodontic clear aligners compared to treatment with conventional metal, fixed appliances. Search methods: We searched without limitations six databases and searched manually the reference lists of relevant studies up to the end of October 2022. Selection criteria: We looked for data from prospective studies that compared OHRQoL, using instruments that had undergone full psychometric validation, between orthodontic patients with clear aligners and labial, fixed, metal orthodontic appliances. Data collection and analysis: We extracted the data from the located studies, and we assessed the risk of bias with the Cochrane Collaboration suggested tools. The quality of available evidence was based on the GRADE approach. Results: Three studies were identified. OHRQoL was impacted less by clear aligners compared to treatment with conventional labially placed, fixed, metal appliances. The exploratory meta-regression, with the time point of assessment as predictor, did not reveal any statistically significant effect. The quality of the available evidence ranged from very low to low. Conclusions: According to the exploratory synthesis of the limited available dataset, treatment with clear aligners could be associated with better OHRQoL ratings compared to treatment with conventional labially placed, metal, fixed appliances. However, the quality of the presented evidence renders further high-quality studies warranted to be able to reach safer conclusions.

Purpose: The use of large language models (LLMs) in generative artificial intelligence (AI) is rapidly increasing in dentistry. However, their reliability is yet to be fully founded. This study aims to evaluate the diagnostic accuracy, clinical applicability, and patient education potential of LLMs in paediatric dentistry, by evaluating the responses of six LLMs: Google AI’s Gemini and Gemini Advanced, OpenAI’s ChatGPT-3.5, -4o and -4, and Microsoft’s Copilot. Methods: Ten open-type clinical questions, relevant to paediatric dentistry were posed to the LLMs. The responses were graded by two independent evaluators from 0 to 10 using a detailed rubric. After 4 weeks, answers were reevaluated to assess intra-evaluator reliability. Statistical comparisons used Friedman’s and Wilcoxon’s and Kruskal–Wallis tests to assess the model that provided the most comprehensive, accurate, explicit and relevant answers. Results: Variations of results were noted. Chat GPT 4 answers were scored as the best (average score 8.08), followed by the answers of Gemini Advanced (8.06), ChatGPT 4o (8.01), ChatGPT 3.5 (7.61), Gemini (7,32) and Copilot (5.41). Statistical analysis revealed that Chat GPT 4 outperformed all other LLMs, and the difference was statistically significant. Despite variations and different responses to the same queries, remarkable similarities were observed. Except for Copilot, all chatbots managed to achieve a score level above 6.5 on all queries. Conclusion: This study demonstrates the potential use of language models (LLMs) in supporting evidence-based paediatric dentistry. Nevertheless, they cannot be regarded as completely trustworthy. Dental professionals should critically use AI models as supportive tools and not as a substitute of overall scientific knowledge and critical thinking.

Summary: Background: Intraoral scanners have become an increasingly popular alternative to conventional impression methods. Although their accuracy and validity have been examined thoroughly, patient-reported information including experiences, preferences, and satisfaction has not yet been investigated in a systematic way. Objective: The objective of this systematic review is to investigate the available data and appraise the evidence on patient-reported experiences and preferences following impression taking with intraoral scanners. Search methods: Unrestricted search of seven databases (Pubmed, CENTRAL, Cochrane reviews, Scopus, Web of Science, Clinical Trials, and ProQuest) and grey literature were conducted until October 2020. Detailed search strategies were developed for each database. Selection criteria: Studies involving individuals of any gender or age, subjected to full arch impression taking with conventional and intraoral scanning methods were eligible for inclusion. Data collection and analysis: Following the retrieval and selection of the studies, data extraction was performed. Risk of bias assessment was performed using the RoB 2 and ROBINS-I tools. Results: From the initially identified records, nine studies [eight crossover (two of them randomized) and one parallel group] were eventually included in the present systematic review. Randomized studies were shown, overall, to have some concerns regarding bias, whereas the non-randomized studies were found to be at serious risk, mainly because of bias due to confounding. All studies demonstrated some benefit in favour of intraoral scanning compared with conventional techniques. More positive feelings were generally observed with the intraoral scanners regarding smell, taste, sound, vibration, nausea, and queasiness. Overall, comfort assessment mostly favoured digital methods. No differences were found concerning the level of anxiety between the two methods. Among the included studies, time perception was a parameter leading to contradictory results. Limitations: These emerge due to the nature and characteristics of the information retrieved from the included studies. The validation of the instruments to capture patient-reported outcomes needs to be further elaborated. Conclusions: Intraoral scanners seem to be a promising new asset in the orthodontic office from the perspective of individuals’ experiences and preferences. Nevertheless, to investigate patient-reported outcomes correctly, further high-quality studies are required in the future. Registration: Open Science Framework (https://osf.io/ayug2/)

Summary: Background: Theoretically, root resorption could be modulated by any medication taken that exhibits possible effects on the implicated molecular pathways.Objectives: To systematically investigate and appraise the quality of the available evidence from animal studies, regarding the effect of commonly prescribed systemic medication on root resorption associated with orthodontic tooth movement.Search methods: Search without restrictions in eight databases (PubMed, Central, Cochrane Database of Systematic Reviews, SCOPUS, Web of Science, Arab World Research Source, ClinicalTrials.gov, ProQuest Dissertations and Theses Global) and hand searching until April 2018 took place. One author developed detailed search strategies for each database that were based on the PubMed strategy and adapted accordingly.Selection criteria: Controlled studies investigating the effect of systemic medications on root resorption associated with orthodontic tooth movement.Data collection and analysis: Following study retrieval and selection, relevant data were extracted and the risk of bias was assessed using the SYRCLE’s Risk of Bias Tool.Results: Twenty-one studies were finally identified, most of which at unclear risk of bias. Root resorption was shown to increase in Vitamin C treated animals in comparison with the control group, whereas a comparative decrease was noted after the administration of the alendronate, ibuprofen, growth hormone, low doses of meloxicam, simvastatin, lithium chloride and strontium ranelate. No difference was noted for acetaminophen, aspirin, fluoxetine, atorvastatin, misoprostol, zoledronic acid and zinc. Finally, inconsistent effects were observed after the administration of celecoxib, prednisolone and L-thyroxine. The quality of the available evidence was considered at best as low.Conclusions: The pharmaceutical substances investigated were shown to exhibit variable effects on root resorption. Although the overall quality of evidence provides the clinician with a cautious perspective on the strength of the relevant recommendations, good practice would suggest that it is important to identify patients consuming medications and consider the possible implications.Registration: PROSPERO (CRD42017078208)