Oral cancer (OC) is one of the most common forms of head and neck cancer and continues to have the lowest survival rates worldwide, even with advancements in research and therapy. The prognosis of OC has not significantly improved in recent years, presenting a persistent challenge in the biomedical field. In the field of oncology, artificial intelligence (AI) has seen rapid development, with notable successes being reported in recent times. This systematic review aimed to critically appraise the available evidence regarding the utilization of AI in the diagnosis, classification, and prediction of oral cancer (OC) using histopathological images. An electronic search of several databases, including PubMed, Scopus, Embase, the Cochrane Library, Web of Science, Google Scholar, and the Saudi Digital Library, was conducted for articles published between January 2000 and January 2023. Nineteen articles that met the inclusion criteria were then subjected to critical analysis utilizing QUADAS-2, and the certainty of the evidence was assessed using the GRADE approach. AI models have been widely applied in diagnosing oral cancer, differentiating normal and malignant regions, predicting the survival of OC patients, and grading OC. The AI models used in these studies displayed an accuracy in a range from 89.47% to 100%, sensitivity from 97.76% to 99.26%, and specificity ranging from 92% to 99.42%. The models’ abilities to diagnose, classify, and predict the occurrence of OC outperform existing clinical approaches. This demonstrates the potential for AI to deliver a superior level of precision and accuracy, helping pathologists significantly improve their diagnostic outcomes and reduce the probability of errors. Considering these advantages, regulatory bodies and policymakers should expedite the process of approval and marketing of these products for application in clinical scenarios.

The success of healthcare delivery systems depends on accessibility. This study aimed to assess parental and guardian perceptions of dental health service accessibility among primary school students in Riyadh, Saudi Arabia. A descriptive cross-sectional survey was conducted among the parents and guardians of primary school students. A 23-item questionnaire was developed on the basis of previous studies. Demographic information, accessibility details, and barriers to dental services were recorded. A total of 385 participants responded to the questionnaire. Approximately 46.2 % of students encountered barriers to accessing dental services. Eighty (20.8 %) male students visited dental facilities more often than female students (72 [18.7 %]) within a 6-month period. Approximately 21 % of students had never visited a dental facility. The lowest number of preventive treatments were administered (15.4 %). Parents and guardians reported that private dental facilities were more accessible (185 [48 %]) than government dental facilities. There was an association between dental facilities and difficulties in accessing dental care services (χ2(4) = 42.753; p < 0.001). In conclusion, parents and guardians reported experiencing difficulties accessing dental services. However, parents and guardians must change their perspectives on accessibility, particularly for preventative care, because primary school children received the lowest.

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.

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.