The aim of this systematic review was to investigate current penetration and educational quality enhancements from digitalization in the dental curriculum. Using a modified PICO strategy, the literature was searched using PubMed supplemented with a manual search to identify English-language articles published between 1994 and 2020 that reported the use of digital techniques in dental education. A total of 211 articles were identified by electronic search, of which 55 articles were selected for inclusion and supplemented with 27 additional publications retrieved by manual search, resulting in 82 studies that were included in the review. Publications were categorized into five areas of digital dental education: Web-based knowledge transfer and e-learning, digital surface mapping, dental simulator motor skills (including intraoral optical scanning), digital radiography, and surveys related to the penetration and acceptance of digital education. This review demonstrates that digitalization offers great potential to revolutionize dental education to help prepare future dentists for their daily practice. More interactive and intuitive e-learning possibilities will arise to stimulate an enjoyable and meaningful educational experience with 24/7 facilities. Augmented and virtual reality technology will likely play a dominant role in the future of dental education.

The aim of this in vitro study was to analyze the impact of model aging on the accuracy of 3D-printed dental models. A maxillary full-arch reference model with prepared teeth for a three-unit fixed dental prosthesis was scanned ten times with an intraoral scanner (3Shape TRIOS Pod) and ten models were 3D printed (Straumann P-Series). All models were stored under constant conditions and digitized with a desktop scanner after 1 day; 1 week; and 2, 3, and 4 weeks. For accuracy, a best-fit algorithm was used to analyze the deviations of the abutment teeth (GFaI e.V Final Surface^®). Wilcoxon Rank Sum Tests were used for comparisons with the level of significance set at α = 0.05. Deviation analysis of the tested models showed homogenous intragroup distance calculations at each timepoint. The most accurate result was for 1 day of aging (3.3 ± 1.3 µm). A continuous decrease in accuracy was observed with each aging stage from day 1 to week 4. A time-dependent difference was statistically significant after 3 weeks (p = 0.0008) and 4 weeks (p < 0.0001). Based on these findings, dental models should not be used longer than 3 to 4 weeks after 3D printing for the fabrication of definitive prosthetic reconstructions.

Introduction: This prospective cohort study investigated the potential of digital assessment using intraoral scanning (IOS) combined with software analysis (prepCheck) to evaluate the outcome of repetitive tooth preparation and its influence on the acquisition of motor skills in dental students.Materials and Methods: Twenty‐six students completed 177 full‐crown preparations of the same tooth in six practice sessions followed by a final examination. Preparations were assessed digitally using prepCheck and conventionally by calibrated faculty instructors. In addition, students assessed their own performance and this was compared with the instructors’ assessments.Results: Conventional assessment showed that students preparations improved over time, with 43.5% of students receiving score 2 (highest grade) at the fifth practice session. With the prepCheck assessment, statistically significant improvements indicated by an enlarged area within the tolerance range set at 0.2 mm were found between the first and the second practice session (7.5% improvement; 95% CI: 2.2%, 12.7%, p = 0.006), and between the first run and the final exam preparation (6.7%; 95% CI: 1.7%, 12.5%, p = 0.011). Agreement between instructor/student assessments was best immediately after students received visual feedback using prepCheck (76% agreement; Spearman's rho 0.78).Conclusion: These data indicate that IOS technology was useful for student's self‐evaluation by visual feedback.