Objective: This study aimed to develop a consensus-based set of patient questions on dental implant failure and to compare the clarity, quality, accuracy, reliability, and readability of responses generated by 4 widely used AI chatbots: ChatGPT-4, DeepSeek-R1, Microsoft Copilot, and Google Gemini. Methods: Twenty-three expert-validated questions were derived from the EAO 2021 and ICOI Pisa Consensus reports and independently submitted to each AI model under standardized, non-personalized conditions. Responses were assessed using CLEAR criteria, mGQS, a 5-point accuracy scale, the first 8 DISCERN items, and Flesch-based readability indices. Nonparametric tests were used for intermodel comparisons. Results: AI models demonstrated significant variability in performance. Gemini achieved the highest accuracy ( P <0.001), whereas ChatGPT-4 exhibited the highest reliability based on DISCERN scores. Copilot generated the most structurally fluent responses, whereas DeepSeek-R1 offered the best readability. Although CLEAR and mGQS scores were high across all systems, readability and linguistic complexity varied markedly. Accuracy, clarity, and reliability were strongly correlated, whereas readability displayed the expected inverse association with grade-level demand. Conclusions: AI chatbots hold potential as adjunct tools for patient education on implant failure; however, their performance characteristics differ substantially. Gemini excels in accuracy, ChatGPT-4 in reliability, Copilot in fluency, and DeepSeek-R1 in readability. Model-specific guidance and continued refinement are needed to enhance the clinical usefulness and accessibility of AI-generated patient information.

Oral Health-Related Quality of Life (OHRQoL) assessment is significant in geriatric periodontitis patients, as aging-related functional limitations, system

Background and Objectives: This study is aimed at evaluating periodontal health in patients with psoriasis and investigating its impact on dermatology-specific and oral health-related quality of life. Materials and Methods: A total of 226 individuals were enrolled, including 113 patients with clinically diagnosed psoriasis and 113 age- and gender-matched healthy controls. The periodontal parameters recorded included plaque index (PI), gingival index (GI), probing depth (PD), and clinical attachment loss (CAL). Oral health-related quality of life was assessed using the Oral Health Impact Profile-14 (OHIP-14), while dermatology-specific quality of life was evaluated with the Psoriasis Quality of Life Questionnaire (PQLQ). Psoriasis severity was measured by the Psoriasis Area and Severity Index (PASI). Results: Patients with psoriasis demonstrated significantly poorer periodontal parameters compared to controls, with higher PI (p = 0.006), PD (p = 0.001), and CAL (p = 0.041), as well as a lower number of teeth (p = 0.027). No significant differences in GI were observed (p = 0.331). Subdomain analysis of OHIP-14 indicated significantly greater functional limitation in the psoriasis group (p = 0.001), although no differences were detected in other domains. Positive and significant correlations were found among all the OHIP-14 subscales in both groups, and PQLQ scores were strongly correlated with OHIP-14 outcomes in the psoriasis group (p < 0.05). PASI scores tended to be higher among patients with periodontitis than those with gingivitis or periodontal health, but this difference did not reach statistical significance (p = 0.257). Conclusions: Psoriasis patients exhibited poorer periodontal status and reduced oral health-related quality of life compared to healthy individuals. However, differences in oral hygiene habits may also have contributed to these findings. Our findings suggest an association between psoriasis and impaired periodontal health, but due to the cross-sectional design, a causal relationship cannot be established.

Aim:Dentin hypersensitivity (DH) is a common dental complaint, and many patients now seek information from AI chatbots. Yet, the accuracy, reliability, and readability of chatbot-generated DH content remain uncertain.Materials and Methods:A consensus-based DH question set was presented to three AI chatbots (ChatGPT-4o, DeepSeek, Copilot) in independent, standardized sessions. Three blinded periodontologists evaluated the responses using CLEAR, mGQS, accuracy scores, DISCERN, and readability metrics (FRE, FKGL). Non-parametric tests compared inter-model differences.Results:Significant differences were found in FKGL (p=0.025), DISCERN (p=0.004), and response length (p

Objectives: This study aimed to evaluate total oxidant status (TOS), total antioxidant status (TAS), oxidative stress index (OSI), arylesterase (ARE), heme oxygenase-1 (HO-1), and nuclear factor erythroid 2–related factor 2 (NRF-2) levels in saliva and serum samples of individuals with Stage III Grade B periodontitis, and to assess their relationship with disease activity and diagnostic potential in the pathogenesis of periodontitis. Materials and methods: Thirty-seven periodontally healthy individuals and thirty-seven patients with Stage III Grade B periodontitis were included in the study. After clinical measurements and sample collection ELISA method was used for analyses of TOS, TAS, OSI, ARE, HO-1, NRF-2 levels. Results: Salivary TAS, serum TAS, serum ARE, serum NRF-2, salivary HO-1 levels were significantly lower in periodontitis patients compared to the healthy control group ( p = 0.015, p = < 0.001, p = 0.031, p = 0.041, p = 0.001). No significant difference was found in salivary and serum TOS, salivary and serum OSI, salivary ARE, salivary NRF-2, serum HO-1 levels ( p = 0.685, p = 0.256, p = 0.146, p = 0.738, p = 0.513, p = 0.910, p = 0.256). Conclusion: Within the limitations of this study, the results suggest that decreased antioxidant capacity, particularly involving HO-1 and NRF-2, may contribute to oxidative stress–related tissue damage in periodontitis. Clinical relevance: Understanding the roles of HO-1 and NRF-2 in the antioxidant defense system provides novel insights into the biological mechanisms underlying periodontal tissue destruction. These biomarkers may help clinicians identify individuals with heightened oxidative stress and increased susceptibility to disease progression, enabling earlier diagnosis and more personalized, targeted therapeutic interventions to improve periodontal health outcomes.