The objective of this study was to evaluate the changes in foreign students' satisfaction with the quality of dental and medical education considering the impact of the war in Ukraine. The present study was organized in the form of a questionnaire-based survey among 300 foreign students of Medical Faculty and Faculty of Dentistry in Ukraine. The questionnaire was ad mistered via Google form in a multiple-choice, closed-ended format. Students' satisfaction with environment safety and comfort (p < 0.05) and with the collaborative learning offered (p < 0.05) statistically decreased during the war. Sixty percent of the variability in the mean of students' satisfaction with the quality of education during the war could be explained by the satisfaction rate before the war. The need of migration from Ukraine had a stronger inverse correlation with education quality (r = -0.58) than the fact of the war itself (r = -0.32). The war in Ukraine has had a negative impact on the educational process of foreign medical and dental students, even though the quality of education was considered by students to be as high as before and during the war. The personal effort of professors, the quality of study materials, and adequate technical support could potentially overcome the negative impact of the war on student satisfaction with the quality of medical and dental education by the online mode, if the academic medium could be protected from the direct impact of the war or if the influence of the war within the university community could be minimized.

Aim: The aim of this study is to compare three large language models (LLMs) (ChatGPT-5, Gemini 2.5 Flash, and DeepSeek-V3.1) in terms of accuracy, understandability, and readability, based on the answers provided to frequently asked endodontic questions. Methodology: Thirty open-ended frequently asked questions were generated using the AlsoAsked and AnswerThePublic websites. Two experienced endodontists scored the accuracy of the responses using a 5-point Likert scale. The understandability of the responses was analyzed using the Patient Education Materials Assessment Tool for Printed Materials (PEMAT-P) tool. Readability was assessed using the Flesch-Kincaid Readability Score (FRES), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Simple Measure of Gobbledygook (SMOG), and Coleman-Liau Index (CLI). Group comparisons were performed using the ANOVA/Kruskal-Wallis test, followed by post-hoc Dunn-Bonferroni tests. Results: Inter-rater agreement was excellent (accuracy ICC: 0.908–0.917; reliability ICC: 0.992–0.995; all p < 0.001). A significant difference was found between the models in terms of accuracy (p < 0.001): DeepSeek-V3.1 (4.63 ± 0.81) scored the highest and performed significantly better than ChatGPT-5 (3.93 ± 0.79) and Gemini 2.5 Flash (3.67 ± 0.76). No significant difference between ChatGPT-5 and Gemini 2.5 Flash (p > 0.05). The understandability (PEMAT-P) scores were similar (p = 0.683), and all models scored above 70% (ChatGPT-5, 77.46%; Gemini, 76.04%; DeepSeek-V3.1, 77.57%). Differences were found in readability metrics: DeepSeek-V3.1 scored higher than ChatGPT-5 in FRES (p = 0.044); Gemini scored higher than DeepSeek-V3.1 in FKGL (p = 0.001); in GFI, Gemini 2.5 Flash scored higher than both ChatGPT-5 (p = 0.036) and DeepSeek-V3.1 (p < 0.001); in SMOG, Gemini outperformed DeepSeek-V3.1 (p = 0.003); and in CLI, ChatGPT-5 was higher than DeepSeek-V3.1 (p = 0.004). No significant correlation was found between readability and understandability (p > 0.05). Conclusion: DeepSeek-V3.1 outperformed ChatGPT-5 and Gemini 2.5 Flash in terms of accuracy. While all models produced similar scores above the PEMAT-P understandability threshold (70%), there were significant differences in readability metrics; furthermore, no model consistently reached the recommended 6th–8th grade level.

The article presents a case of spontaneous recession repair in a male patient with Class II malocclusion, division 1, after orthodontic treatment with aligners. The difference in digital recession depth was measured before and at the end of treatment by means of automatic intraoral scans superimposition within adapted software while using “Cross section” and “Measuring” instruments. Digital analysis of intraoral scans obtained before and at the end of treatment has revealed that recessions within the area of teeth 1.5, 1.4, 1.3, 1.2, 1.1, 2.1, 2.2, 2.3, 2.4, and 2.5 have improved, and recession depth reduced by mm, mm, mm, mm, mm, mm, mm, mm, mm, and mm, appropriately. The present case report emphasizes that orthodontic correction of altered tooth position (angulation, inclination, and rotation) under certain clinical conditions may be considered as an effective method for soft tissue contour optimization in cases when pre-treatment tooth position could be interpreted as a causative factor or associated with diagnosed recession. The following outcomes could be related, but not limited to creeping attachment mechanism, bone-housing centering effects, optimization of occlusal load distribution with ruling out peak zones of strain accumulation, and mucogingival stress leveling. Due to the authors’ knowledge, the present case report is the first one where the signs of spontaneous recession repair after orthodontic treatment were evidenced with the intraoral scans and quantified by the specifically implemented digital analysis approach.

Objective: To approbate the complex approach for assessment of second molar mesialization outcomes with the use of orthodontic mini-implants. Material and Methods: The sample consisted of 62 patients, divided into study (n=32) and control group (n=30). Mesialization procedure in the study group was conducted with the use of braces system and orthodontic mini-implants as additional anchorage devices, while in control group mesialization was provided only with the use of the brace system. Dynamic registration of bone level changes and the entire range of tooth movement were carried out on digital orthopantomograms obtained with the use of Planmeca ProMax 2D. Results: Findings of orthopantomographic (OPG) analysis have shown that cases of second molar mesialization with the use of mini-implants as temporary anchorage characterized with more stable conditions of bone levels around displaced teeth compare to cases, where mesialization was provided only with the use of braces systems without any additional anchorage. The terms of treatment in the study group with the use of dental mini-implants as the anchorage was reduced by 8.8 ± 0.12 months compared to the control group (p<0.05). Conclusion: The use of orthodontic miniimplants as anchorage constructions during the mesialization of the mandibular second molars contributes to the reduction of treatment duration and support the more prognostic movement of teeth, that does not provoke significant pathological changes in the levels of the surrounded alveolar ridge and minimize the risk of associated periodontal complication occurrence.

Background. Overall pool of studies regarding artificial intelligence (AI) implementation in dentistry is increasing every year, while possibilities for using AI methods within everyday endodontic practice is still quite confined and not always enough affirmed. Objective. To systematize and depict principal data regarding use of virtual artificial intelligence for various endodontic-related clinical purposes. Materials and Methods. Targeted literature search was provided within National Center for Biotechnology Information databases using pre-specified Mesh-terms algorithm. The following information was extracted from each publication during content analysis: diagnostic and treatment planning aspects of endodontic practice for which AI methods could be applied; accuracy levels registered for AI models used for different endodontic-related purposes; limitations of using AI within endodontic practice. Results. AI features could be used in endodontic practice for the following reasons: analysis of root canal morphology, identification of root fractures, verification of periapical lesions, estimation of root canal working length, root canal treatment planning, prediction of pain development during post-treatment period, predication of endodontic interventions success. The most prevalently used artificial intelligence methods for different endodontic diagnostic and treatment planning objectives were the following: convolutional neural network, artificial neurons network, case-based reasoning, deep learning, machine learning, neuro-fuzzy inference system, probabilistic neural network. Conclusion. Main advantage of using AI models in endodontic practice associated with improvement of diagnostic accuracy within reduced amount of time needed for X-ray images and clinical data analysis. AI application for apical foramen detection and working length determination demonstrates the highest level of accuracy compared to AI performance for other clinically related objectives in endodontics.