Background: The use of artificial intelligence in the field of health sciences is becoming widespread. It is known that patients benefit from artificial intelligence applications on various health issues, especially after the pandemic period. One of the most important issues in this regard is the accuracy of the information provided by artificial intelligence applications. Objective: The purpose of this study was to the frequently asked questions about dental amalgam, as determined by the United States Food and Drug Administration (FDA), which is one of these information resources, to Chat Generative Pre-trained Transformer version 4 (ChatGPT-4) and to compare the content of the answers given by the application with the answers of the FDA. Methods: The questions were directed to ChatGPT-4 on May 8th and May 16th, 2023, and the responses were recorded and compared at the word and meaning levels using ChatGPT. The answers from the FDA webpage were also recorded. The responses were compared for content similarity in “Main Idea”, “Quality Analysis”, “Common Ideas”, and “Inconsistent Ideas” between ChatGPT-4’s responses and FDA’s responses. Results: ChatGPT-4 provided similar responses at one-week intervals. In comparison with FDA guidance, it provided answers with similar information content to frequently asked questions. However, although there were some similarities in the general aspects of the recommendation regarding amalgam removal in the question, the two texts are not the same, and they offered different perspectives on the replacement of fillings. Conclusions: The findings of this study indicate that ChatGPT-4, an artificial intelligence based application, encompasses current and accurate information regarding dental amalgam and its removal, providing it to individuals seeking access to such information. Nevertheless, we believe that numerous studies are required to assess the validity and reliability of ChatGPT-4 across diverse subjects.

Background: The objective of our study was to compare the upper and lower surface microhardness and surface changes of Glass Carbomer Cement (GCP) and EQUIA Forte (EF) in different thickness after thermo-light curing durations and aging. Methods: A total of 504 samples (5 mm-diameter) were prepared by using GCP-252 (GCP Dental, and Vianen, Netherlands) and EF-252 (EQUIA Forte, GC, Tokyo, Japan). Three different thickness samples (2, 4, and 6 mm) were prepared with 84 samples in each subgroup. The samples were prepared by three curing procedures (Non-exposed, 60s, 90s). Their varnishes were applied to the upper surfaces of half of each subgroup (n = 7). The upper microhardness measurements were evaluated before and after aging. To compare the effect of different thicknesses, the bottom surfaces of the samples were evaluated before aging in terms of microhardness measurements. Also, the upper surfaces were analyzed in the SEM before and after aging. Results: The upper surface values of all the samples were higher than the bottom values (p < 0.05). There were no significant differences between the varnished and non-varnished samples in both materials (p > 0.05). Although this increase was not significant in some groups, temperature variations increased the surface microhardness values of both materials except for the non-exposed-varnished EF samples. The highest microhardnesses values were recorded in the non-exposed-varnished EF (125.6 ± 6.79) and unvarnished GCP (88.1 ± 7.59) samples which were thermo-light cured for 90 s before aging. The bottom hardness values were affected by thickness variations in both GCP and EF materials (p < 0.05). The sample deformations and microcracks after aging were greater than before in all the materials. Thermo-light curing in 90 s to the samples reduced the cracks in both the materials before and after aging. Conclusions: Thermal aging adversely affected the microhardness of the materials, which is important for clinical success. The thermo-light curing process improved the microhardness of the GCP group without varnish application. Varnish application increased the microhardness of the EF group without applying thermo-light curing. The microhardness of the bottom surfaces decreased with increasing thickness. The thermo-light curing did not increase the bottom surface microhardness of all the samples.

Objectives; Getting information on health issues from social media applications provides benefits to patients; however, different types of content can lead to the development of inappropriate norms. This study therefore aimed to evaluate the content and information quality of Instagram® posts about teeth whitening. Materials and Methods; The first 100 posts were included in the three search periods, which started in October 2022 and were carried out at two-week intervals. (n=300) The #teethwhitening hashtag, which was determined as a trend topic in each search period, was used. The content of the posts was analyzed over eight questions, whereas their general information quality was analyzed according to the modified DISCERN analysis on seven questions. The demographic data of the posts were evaluated in two parts: a) account and b) data of the post. The distribution of the user questions and other (#) hashtags mentioned in the posts were also analyzed. Results; 191 posts were excluded from the research. 42% (n=46) of the posts had “low” information content. In addition, the mean value of the content was 2.77 (min 0, max 8/n=109). The mean score in the modified DISCERN score was 11.83 (min 7, max 35/n=109). 53% (n= 57) of the posts were related either to prosthetic treatments alone or to combined treatments that included prosthetic procedures. Most of the posts mentioned the hashtag "#veneer/s". Questions such as cost, duration of treatment, and appointment procedures were also asked as comments under the posts. Conclusion; It can be stated that the content and information quality of Instagram® posts about #teethwhitening are insufficient and may direct patients to more interventional invasive treatment options.

Objective: It is aimed to evaluate the color changes of upper central incisor teeth with different root maturation levels after 15 min of dehydration and rehydration.Materials and Methods: Using the Cvek classification, 60 participants were divided into five groups. A spectrophotometer, equipped with a specially designed jig, was utilized for color measurements. Following isolation, color measurements were conducted at the dehydration process intervals of baseline, 1st, 2nd, 3rd, 5th, 7th, 10th, and 15th minutes. After mouth rinsing, measurements were repeated at the same time intervals during the rehydration process. CIEDE2000 (ΔE₀₀) values and the Whiteness Index (WI_D) were obtained to compare the color changes between the initial readings and each interval. Friedman and Repeated Measures ANOVA were applied to compare classes by time during the dehydration and rehydration periods, as well as ANOVA and Kruskal‐Wallis tests to compare by classes for each time (p < 0.05).Results: Significant differences emerged in the mean ΔE₀₀ values during dehydration and rehydration periods across all Cvek Classes (p < 0.05). While no statistically significant differences were observed among Classes at different minutes during the rehydration period (p > 0.05), it was particularly noted that the mean ΔE₀₀ values of Classes 2, 3, and 5 at the 2nd minute of dehydration were statistically significantly higher than the mean of Class 1 (p < 0.05). However, when evaluating the starting and ending measurements of both dehydration and rehydration processes, and the overall change in ΔE₀₀ and ΔWI_D, no significant variations were detected across the Classes (p > 0.05).Conclusions: In teeth that have just erupted and are still undergoing root development, dehydration in the initial minutes results in noticeable color changes. However, the change in ΔE₀₀ and ΔWI_D of the teeth was found to be similar after the dehydration and rehydration processes.Clinical Significance: According to the Cvek classification, the lower color change in Class 1 teeth within the first 2 min of dehydration and the need for more than 15 min of rehydration for immature teeth with an open apex to return to their initial state after a 15‐min dehydration are crucial for clinicians in terms of color assessment timing.

The short form of the Dentin Hypersensitivity Experience Questionnaire (DHEQ-15) is a valid and reliable tool for Oral Health-Related Quality of Life (OHRQoL) studies. The aim of this study was cross-culturally adapt and validate the English version of DHEQ-15 for the Turkish language. The Turkish version of the DHEQ-15 was developed using the forward-backward translation method. Two independent translators translated it from English to Turkish, followed by back-translation into English. A pilot study was conducted with 30 patients with dentin hypersensitivity (DH). The sample consisted of 150 individuals with DH and 150 without. DH was assessed using the Schiff Sensitivity Scale by applying air to the cervical area of the tooth for 1 s from a distance of 1 cm. A total of 300 individuals completed the Turkish DHEQ-15 in the waiting room. Subsequently, 30 randomly selected individuals with DH completed the questionnaire again after 7–10 days to assess test-retest reliability. Participants provided an overall oral health rating assessment on a 6-point scale. The reliability of the DHEQ-15 was assessed using internal consistency and test-retest methods. Exploratory Factor Analysis (EFA) identified its domains, while construct validity was evaluated through cross-cultural (Confirmatory Factor Analysis- CFA), discriminative (Mann-Whitney test) and convergent (Spearman correlation) validity. The demographic characteristics of the sample included 137 men and 163 women, with an average age of 36. The Turkish DHEQ-15 demonstrated excellent internal consistency (α = 0.970) and outstanding test-retest reliability (r = 0.920, p < 0.001), indicating an excellent correlation. Confirmatory factor analysis confirmed the unidimensional scale structure of the Turkish DHEQ-15 (p = 0.000). A statistically significant negative correlation was found between the DHEQ-15 score and the overall oral health rating assessment (p < 0.05). Participants with DH had a significantly higher DHEQ-15 score compared to those without DH (p < 0.001). This study provides evidence supporting the cross-cultural validity and reliability of the Turkish DHEQ-15.

Aim: Prognosis prediction after tooth avulsion is challenging due to multiple interacting clinical factors. The agreement of conversational large language models with expert dentists, their sensitivity to prompt format, and the stability of their outputs over time remain unclear. This study compared four conversational large language models with expert assessments using standardized simulated avulsion scenarios and evaluated prompt format effects and short‐term stability. Material and Methods: A simulation‐based observational study was conducted using 120 standardized synthetic avulsion scenarios created by three experienced dentists. Each scenario was converted into four standardized prompt formats while preserving identical clinical content. Four conversational large language models (ChatGPT‐4o, Gemini, Claude, and DeepSeek) evaluated each case twice at a 48‐h interval, providing numeric scores on a 0–12 scale and ordinal prognosis categories. Expert reference values were obtained from blinded independent dentist assessments with subsequent consensus. Agreement, prompt‐related variability, and short‐term stability were analyzed using nonparametric and agreement‐based methods. Results: Meaningful agreement with expert prognosis assessments was observed across models for both numeric scores and ordinal categories. Gemini and ChatGPT‐4o showed the most balanced performance, whereas Claude preserved relative risk ordering with greater deviation in absolute severity, and DeepSeek demonstrated lower categorical concordance. Prompt format significantly influenced outputs, with the clinical‐practical format showing the closest alignment and the narrative format the weakest. Short‐term stability was generally maintained, although small but statistically detectable shifts occurred in some model–format combinations. Conclusions: Conversational large language models can generate avulsion prognosis estimates that meaningfully align with expert dentists under standardized conditions. However, performance is model‐dependent and strongly influenced by information structure. These systems should be used as supportive tools rather than stand‐alone decision makers, and further studies using real‐world data are needed to confirm clinical utility and stability. Clinical Relevance: Under standardized conditions, conversational AI models—particularly Gemini and ChatGPT—may support clinicians as secondary decision aids in structured prognosis estimation for avulsed permanent teeth.