Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency in their approach, the researchers followed the diagnostic test accuracy guidelines outlined in PRISMA-DTA for this systematic review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library to identify relevant articles published between the years 2000 and 2024. A total of 26 articles that satisfied the inclusion criteria were subjected to a risk of bias assessment using QUADAS-2, which revealed a flawless risk of bias in both arms for the patient-selection domain. Additionally, the certainty of evidence was evaluated using the GRADE approach. AI technology has primarily been utilized for automated age estimation through tooth development stages, tooth and bone parameters, bone age measurements, and pulp–tooth ratio. The AI models employed in the studies achieved a remarkably high precision of 99.05% and accuracy of 99.98% in the age estimation for models using tooth development stages and bone age measurements, respectively. The application of AI as an additional diagnostic tool within the realm of age estimation demonstrates significant promise.

Large language models (LLMs) have gained popularity among dental students for generating subject-related answers. However, their widespread use raises significant concerns about misinformation. This systematic review aims to critically evaluate studies assessing the performance of LLMs in dentistry. A comprehensive electronic search was conducted in PubMed/Medline, Scopus, Embase, Web of Science, Google Scholar, and the Saudi Digital Library to identify studies published up to September 2024. The study quality was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). A total of 2030 studies have been identified. After removing 907 duplicate records, 1123 studies remained for screening. Ultimately, 31 studies met the inclusion criteria. Approximately half of these studies were classified as "high risk," while the remainder were classified as "low risk." The applicability of the findings was rated as "low concern." The primary limitations of LLMs include their inability to specify information sources and their tendency to generate fabricated citations. Based on this review, LLMs hold promise as supplementary educational tools in dentistry. Evidence indicates that students using LLMs may achieve improved academic performance compared to traditional methods. However, concerns about occasional inaccuracies and unreliable citations underscore the need for further research, integration with validated sources, and adherence to ethical guidelines. Ultimately, LLMs should be viewed as complementary tools within dental education, with careful consideration of their limitations.

Background/Objectives: Dental professionals engage in a variety of dental procedures within a confined workspace that is often challenging to access and navigate. This environment frequently results in static, asymmetrical, and inappropriate postures, which can lead to muscular imbalances and cause pain or damage to the musculoskeletal system. Such issues can adversely affect the dental workforce, resulting in increased absenteeism, reduced productivity, disability, and premature retirement from the profession. Therefore, the objective of this study was to develop and evaluate the performance of an Artificial Intelligence (AI)-based deep learning model designed to assess dental ergonomics. Methods: An AI-based Dental Ergonomic Posture Assessment Model [SBK-DentErgo] was developed through the strategic integration of YOLOv11 and MediaPipe. Model training and validation were conducted using 500 photographs of dental professionals performing procedures on patients, captured from both frontal and sagittal planes. In the initial phase of the study, two calibrated evaluators assessed 50 photographs, demonstrating excellent agreement. In the subsequent phase, five dental specialists, along with the AI model, evaluated the same set of photographs, and the results were recorded. Results: AI-based model demonstrated excellent agreement with that of calibrated evaluators (Kappa = 0.922, p = 0.000). The reliability of AI-based scores was also consistent (ICC = 1.000, p = 0.000). Human evaluation of ergonomic posture exhibited very low sensitivity (20.5%) compared to AI, which showed very high sensitivity (97%). The specificity of human evaluation was also extremely low (9.1%) in contrast to AI (85.7%). The AI model (AUC = 0.917, 95% CI 0.762–1.000) could serve as the ‘gold standard’ in evaluating dental operator ergonomics. Conclusions: This AI model demonstrated exceptional performance in evaluating the working postures of dental professionals, surpassing experienced specialists in both sensitivity and specificity. The model provides real-time feedback, enabling dentists to conduct self-assessments and correct their posture immediately, thereby preventing postural issues.

The use of electronic cigarettes, or “e-cigarettes”, among youths has sparked worries about the possibility of nicotine dependence as a serious public health issue. Dental practitioners play a critical role in helping their patients quit smoking. Dental schools across the globe have policies encouraging their students to help patients quit smoking. Current research, however, indicates that a significant portion of dental students smoke combustible cigarettes and use e-cigarettes. According to studies, using e-cigarettes has resulted in the subsequent initiation of combustible cigarette smoking among its users. The aim of this study was to determine the association between the use of electronic cigarettes and the subsequent initiation of combustible cigarette smoking among dental students who were not attitudinally susceptible to smoking combustible cigarettes. A longitudinal cohort study was conducted among 121 study participants who were never combustible cigarette users and were attitudinally non-susceptible to smoking at baseline. At baseline, 66 (54.6%) study participants were categorized as e-cigarette users who were attitudinally non-susceptible to combustible cigarette smoking, and 55 (45.4%) study participants were categorized as non-users who were attitudinally non-susceptible to combustible cigarette smoking. The initiation of combustible cigarette smoking was assessed at 6- and 12-month intervals. Binomial regression analysis of the outcome at the end of one-year follow-up, when analyzed with independent variables, revealed a significant influence of e-cigarette use on taking up combustible cigarette smoking [Relative Risk: 9.395; 95% CI: 3.03–29.04]. Chi-squared analysis of independent variables revealed e-cigarette use to be significantly associated with fathers’ education level (p = 0.00), parental cigarette smoking status (p = 0.00), cigarette smoking among friends (p = 0.00), and family income (p = 0.00). E-cigarette users are more likely to believe it to be healthier (p = 0.00) than combustible smoking. In the present study, e-cigarette usage demonstrated a significant influence on taking up combustible cigarette smoking among its users. Educational institutions should implement stringent policies and regulations to prevent health professionals from using these products.

Bibliometric is a quantitative assessment method to identify prevailing trends and patterns of publications in a particular field of study. Systematic Reviews (SRs) give an in-depth study of the literature and the most comprehensive overview of the desired research subject. This study aimed to review the bibliometric properties of SRs published by the dentists of Saudi Arabia. Relevant dataset of SRs on dentistry with at least one author affiliated with Saudi Arabia from PubMed database was obtained. Each document of the retrieved data was screened based on the inclusion and exclusion criteria. The citation metric was collected from Google Scholar. A total of 625 SRs published from the year 2007 to 2023 were included. The average number of citations for each of these SRs is 25.54. Though a low productivity was observed until 2017, the number of SRs published has increased in the previous three years. SRs in Prosthodontics were the most prevalent. The SRs with the first author from Saudi Arabia and internationally collaborated gained a higher citation impact. Most of the SRs were published in the Saudi Dental Journal. Authors from King Saud University contributed to more than a quarter of the SRs. The two nations that collaborated the most SRs were found to be United States and India. Over the last three years, there has been significant progress in the publishing of SRs from Saudi Arabia. The bibliometric parameters identified in this study support researchers in understanding the current status of SRs publication. There is a need to increase awareness about the importance of high-evidence research in dentistry and enhance the training opportunities as well as research incentives for this kind of scientific production.

Purpose: Dental magnifying loupes (DMLs) allow better visualization of the operative field and operator’s seating postures. This study aimed to investigate factors influencing the usage and selection of DMLs among dentists and determine the self-reported musculoskeletal pain and perception regarding DMLs usage. Methods: A validated self-reported questionnaire of 19 items assessing participant’s characteristics and DMLs usage was distributed to individuals who fit the inclusion criteria: practicing general dentists/dental intern and resident/specialized dentist in various dental specialties. The data were collected and then statistically analyzed using comparisons tests (chi-square/Fisher’s exact test) and logistic regression with significance level (p-value) below 0.05. Results: The response rate for this study’s questionnaire was 97.33%. There is a significant association between the use of DMLs and training using them in dental school (p < 0.002). Using DMLs in endodontic treatment is significantly associated with the general dentist professional classification. Specialized dentists are 1.94-times more likely to use DMLs than general dentists (p < 0.001). The majority (50%) of DML users believe that DMLs are best used during undergraduate training. Using DMLs for “better vision” was the most selected (63.64%) option by DMLs users, followed by “increased clinical efficacy” (61.36%), “improved patient care” (50%), and “better ergonomics” (47.73%). Of dentists who did not use DMLs, 66.90% reported that DMLs being expensive was a limitation to their usage. Almost 91% dentists who have neck and shoulder discomfort (n = 219) do not use DMLs. Conclusion: DMLs are under-utilized by dentists. The major factors limiting their use are the high cost and lack of training regarding their use in dental school. Addressing these two factors may contribute to a positive shift in the utilization of DMLs by a larger number of dentists.