Mehran Moghbel

Aims: This study evaluated the quality, accuracy, and readability of responses generated by four Artificial Intelligence (AI) chatbots based on large language models (LLMs): ChatGPT-5.2, Gemini 3, DeepSeek-V3.2, and Grok-4.1, when responding to expert-generated questions in anterior implant dentistry. The aim was to evaluate their potential role as educational and adjunct informational tools in treatment planning and esthetic zone management, while also examining the readability of the generated responses.Methods: Thirty-six standardized questions covering diagnosis, esthetic risk assessment, implant positioning, surgical planning, peri-implant soft tissue management, esthetic complications, and preventive strategies were developed by three prosthodontists experienced in implant dentistry. Responses from ChatGPT, Gemini, DeepSeek, and Grok were independently evaluated by three experts using the modified DISCERN (mDISCERN) score, Global Quality Score (GQS), and a five-point Accuracy Score. Misinformation and Harm scores were also recorded. Repeated-measures comparisons were performed using the Friedman test with Wilcoxon signed-rank post hoc analysis and Bonferroni correction.Results: ChatGPT demonstrated the highest mean scores for informational reliability (mDISCERN: 3.33±0.72) and accuracy (3.56±0.84); however, differences in accuracy were not statistically significant. Gemini showed the highest numerical GQS value (GQS: 3.75±0.77); however, the overall effect size was small, and no GQS pairwise comparison remained statistically significant after adjustment. Grok showed intermediate performance, whereas DeepSeek showed lower numerical reliability scores, with significant pairwise differences identified only in comparison with ChatGPT and Gemini. Significant differences were observed for mDISCERN [χ² (3)=17.52, p0.05). Readability analysis showed a small but statistically significant difference in FRES among the chatbots, with Gemini demonstrating easier readability than Grok after Bonferroni adjustment, whereas FKGL did not differ significantly among the models; overall, the responses required a high-school to early undergraduate reading level.Conclusion: AI chatbots can generate information with potential clinical relevance in anterior implant dentistry; however, variability in informational reliability persists. Although ChatGPT showed slightly higher numerical scores for some outcomes, the observed differences were generally small, and no chatbot demonstrated clear superiority across all evaluated measures. Expert supervision therefore remains essential before integrating such tools into clinical education.

Background: This in vitro study evaluated the effect of hydroxyapatite nanoparticle (nano-HAp) incorporation on surface roughness and Vickers hardness of a 3D-printed crown resin after thermocycling. Methods: Disk-shaped specimens (N = 84) were modified and fabricated with 0%, 1%, 2%, and 3% nano-HAp. Surface roughness (Ra) and Vickers hardness (VHN) were measured before and after thermocycling (5000 cycles). Surface morphology was qualitatively assessed using FE-SEM. Data were analyzed using two-way mixed-design ANOVA (α = 0.05). Results: Thermocycling increased surface roughness and reduced hardness in all groups. Ra values were highest in the 3% nano-HAp group after thermocycling (1.16 ± 0.47 µm). Baseline Vickers hardness differed significantly among nano-HAp concentrations, and hardness decreased after thermocycling in all groups; however, the 3% nano-HAp group exhibited the highest post-thermocycling hardness values (24.66 ± 1.51 VHN), which should be interpreted in the context of its higher baseline hardness. FE-SEM observations suggested increased surface irregularities with higher nano-HAp concentrations after thermocycling. Conclusions: Nano-HAp incorporation influenced both surface and mechanical properties, with 3% concentration showing higher hardness after aging but increased roughness.

Aim The aim of this study was to determine the career plans, specialty preferences, and perceptions of Pediatric Dentistry (Pedodontics) among students at Biruni University Faculty of Dentistry, and to reveal the relationship of these factors with students' grade level and demographic characteristics. Material and method This descriptive and cross-sectional survey study included 268 students. Data were collected online via Google Forms between October 13 and October 17, 2025. Statistical analyses included Pearson's Chi-Square and Fisher's Exact tests. Results Statistically significant associations were found between specialty preference and gender (p = 0.015), knowledge of postgraduate education upon university entry (p=0.020), career motivations (p = 0.037), perception of the best specialty for personal quality of life (p< 0.001), perception of the most sufficient department for patient care (p=0.003), and the likelihood of choosing Pedodontics as a career (p