Background/Objectives: Artificial intelligence (AI) has gained substantial prominence in pediatric dentistry, offering new opportunities to enhance diagnostic precision and clinical decision-making. AI-based systems are increasingly applied in caries detection, early childhood caries (ECC) risk prediction, tooth development assessment, mesiodens identification, and other key diagnostic tasks. This systematic review and meta-analysis aimed to synthesize evidence on the diagnostic performance of AI models developed specifically for pediatric dental applications. Methods: A systematic search was conducted in PubMed, Scopus, Web of Science, and Embase following PRISMA-DTA guidelines. Studies evaluating AI-based diagnostic or predictive models in pediatric populations (≤18 years) were included. Reference screening, data extraction, and quality assessment were performed independently by two reviewers. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using random-effects models. Sources of heterogeneity related to imaging modality, annotation strategy, and dataset characteristics were examined. Results: Thirty-two studies met the inclusion criteria for qualitative synthesis, and fifteen were eligible for quantitative analysis. For radiographic caries detection, pooled sensitivity, specificity, and AUC were 0.91, 0.97, and 0.98, respectively. Prediction models demonstrated good diagnostic performance, with pooled sensitivity of 0.86, specificity of 0.82, and AUC of 0.89. Deep learning architectures, particularly convolutional neural networks, consistently outperformed traditional machine learning approaches. Considerable heterogeneity was identified across studies, primarily driven by differences in imaging protocols, dataset balance, and annotation procedures. Beyond quantitative accuracy estimates, this review critically evaluates whether current evidence supports meaningful clinical translation and identifies pediatric domains that remain underrepresented in AI-driven diagnostic innovation. Conclusions: AI technologies exhibit strong potential to improve diagnostic accuracy in pediatric dentistry. However, limited external validation, methodological variability, and the scarcity of prospective real-world studies restrict immediate clinical implementation. Future research should prioritize the development of multicenter pediatric datasets, harmonized annotation workflows, and transparent, explainable AI (XAI) models to support safe and effective clinical translation.

Background/Aim: Furcation lesions in primary molars are critical in pediatric dentistry, often guiding treatment decisions between root canal therapy and extraction. This study introduces a deep learning-based clinical decision-support system that directly maps radiographic lesion characteristics to corresponding treatment recommendations—a novel contribution in the context of pediatric dental imaging, also represents the first integration of panoramic radiographic classification of primary molar furcation lesions with treatment planning in pediatric dentistry. Materials and Methods: A total of 387 anonymized panoramic radiographs from children aged 3–13 was labeled into five distinct bone lesion categories. Three object detection models (YOLOv12x, RT-DETR-L, and RT-DETR-X) were trained and evaluated using stratified train-validation-test splits. Diagnostic performance was assessed using precision, recall, mAP@0.5, and mAP@0.5–0.95. Additionally, qualitative accuracy was evaluated with expert-annotated samples. Results: Among the models, RT-DETR-X achieved the highest performance (mAP@0.5 = 0.434), representing modest but clinically promising diagnostic capability, despite the limitations of a relatively small, single-center dataset. Specifically, RT-DETR-X achieved the highest diagnostic accuracy (mAP@0.5 = 0.434, Recall = 0.483, Precision = 0.440), followed by YOLOv12x (mAP@0.5 = 0.397, Precision = 0.442) and RT-DETR-L (mAP@0.5 = 0.326). All models successfully identified lesion types and supported corresponding clinical decisions. The system reduced diagnostic ambiguity and showed promise in supporting clinicians with varying levels of experience. Conclusions: The proposed models have potential for standardizing diagnostic outcomes, especially in resource-limited settings and mobile clinical environments.

Large language model-based (LLM) chatbots are increasingly integrated into healthcare communication, offering accessible and interactive information. These artificial intelligence (AI) tools have the potential to influence caregiver health behaviors when tailored to user needs and literacy levels. In pediatric dentistry, fluoride remains a cornerstone of caries prevention but is also subject to public concerns and online misinformation, underscoring the need for reliable digital communication. This observational and exploratory study evaluated the performance of three advanced AI chatbots—ChatGPT-4.o, Google Gemini Pro, and DeepSeek V3—in providing fluoride-related information to parents and caregivers in the context of pediatric oral health. Twenty fluoride-related questions, derived from American Academy of Pediatric Dentistry (AAPD) guideline themes, were presented to each chatbot in standardized sessions. Responses were independently evaluated by three blinded reviewers using validated tools: EQIP, DISCERN, Global Quality Scale (GQS), Flesch Reading Ease Score (FRES), Flesch-Kincaid Reading Grade Level (FKRGL), and iThenticate similarity index. These instruments assessed quality, reliability, readability, and originality. Inter-rater reliability was confirmed with intraclass correlation coefficients (ICCs). Statistical analyses were conducted using ANOVA or Kruskal–Wallis tests with appropriate post-hoc methods. ChatGPT-4.o achieved significantly higher EQIP (M = 4.32, SD = 0.43) and DISCERN (M = 4.20, SD = 0.48) scores than Gemini Pro and DeepSeek V3 (p < 0.001), indicating superior reliability and informational quality. While FRES (median = 68.5, p = 0.12) and Similarity Index (≤ 10%, p = 0.54) showed no significant differences, ChatGPT consistently produced more readable and original content. FKRGL differences were borderline (p = 0.041) but not retained after correction, and GQS outcomes were comparable. These findings suggest that ChatGPT’s superior performance is not only statistically significant but also practically relevant for enhancing parental comprehension of fluoride use. Among the evaluated models, ChatGPT-4.o demonstrated the clearest and most reliable fluoride communication. Its higher EQIP and DISCERN scores highlight its potential as a supportive tool for caregiver education in pediatric dentistry. Nonetheless, these systems should be implemented cautiously, complemented with professional oversight, and continuously validated to prevent misinformation and ensure safe clinical integration.

Objective: Electromagnetic Field sources which are widely used as part of the operating mechanism occupational exposure occurs with the electricity is generated, distributed or usage in the everyday environment. We aimed to obtain the Extremely Low Frequency-Magnetic Field exposure profile for dental health workers from electrical devices used in dentistry. Methods: Measurements were performed while appliances under working conditions, 10 cm distance with the device for 6 minutes and for 7 days. The highest measured value in every 10 second was recorded. The mean ± SD of minimum and maximum Extremely Low Frequency-Magnetic Field for high-speed dental handpiece with LED, low-speed dental handpiece, model-trimming machine, automatic boil-out unit, steam machine, vacuum device, and polishing machine were recorded in milliGauss. Results: The values which obtains from model-trimming machine and polishing machine were remarkable (809.17 ± 37.70 / 975.00 ± 10.27, 649.33 ± 201.37 / 1367.00 ± 32.17, respectively). Conclusion: The results show that periodic Electromagnetic Field exposure measurements should be conducted to obtain more detailed information about workplace exposures and sources. Keywords: Health, electromagnetic field, extremely low-frequency field, exposure, occupational

This study aimed to evaluate the effects of parents’ sociodemographic characteristics, oral health-related attitudes and behaviors, and oral health l

Background: Although dentists are among high-risk groups for COVID-19 due to their practice requiring to be in close proximity to patients’s oral cavities, there is a clear lack of studies on the frequency of hesitancy towards COVID-19 vaccines and the factors that influence the said hesitancy. This study aims to determine the factors causing hesitancy to receive COVID-19 vaccines in dentists and the frequency of the hesitancy. Methods: The universe of this online cross-sectional survey was 23,664. Participants were then selected from the association’s member list using the numbers table. Dentists were selected by random sampling method and the sample size was 458. SPSS 22.0 for Windows was used for the data analysis. Chi-square test was used in the comparison analysis of categorical variables, and the statistical significance level was set as p<0.05. Results: Of the 458 dentists participating in the study, the mean age was 38±10.5, 133 (29.0%) were male and 325 (71.0%) were female. 59 (12.9%) dentists stated that they have not been vaccinated with the COVID-19 vaccine, 399 (87.1%) participants stated that they have. Conclusions: In this study, 87.1% of dentists in Turkey stated that they have been vaccinated with the COVID-19 vaccine; proving that there is no vaccine hesitation among dentists in Turkey.