Attitudes, knowledge, and perceptions of dentists and dental students toward artificial intelligence: a systematic review.

Objectives: This research was aimed at assessing comprehension, attitudes, and perspectives regarding artificial intelligence (AI) in dentistry. The null hypothesis was a lack of foundational understanding of AI in dentistry. Methods: This systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted in May 2023. The eligibility criteria included cross-sectional studies published in English until July 2023, focusing solely on dentists or dental students. Data on AI knowledge, use, and perceptions were extracted and assessed for bias risk with the Joanna Briggs Institute checklist. Results: Of 408 publications, 22 relevant articles were identified, and 13 studies were included in the review. The average basic AI knowledge score was 58.62 % among dental students and 71.75 % among dentists. More dental students (72.01 %) than dentists (62.60 %) believed in AI's potential for advancing dentistry. Conclusions: Thorough AI instruction in dental schools and continuing education programs for practitioners are urgently needed to maximize AI's potential benefits in dentistry. An integrated PhD program could drive revolutionary discoveries and improve patient care globally. Embracing AI with informed understanding and training will position dental professionals at the forefront of technological advancements in the field.

Prevalence of malocclusion in Turkish children and adolescents: A systematic review and meta-analysis.

OBJECTIVES: The aim of this article is to establish a comprehensive nationwide prevalence of malocclusion traits on the sagittal, vertical, and transverse planes of space in the Turkish population. MATERIAL AND METHODS: A systematic search of PubMed, Scopus, and Web of Science was supplemented by manual searches of Google Scholar and the reference lists of included studies. Original Turkish health studies of any age were included. Strengthening the Reporting of Observational Studies in Epidemiology assessed study quality and bias (STROBE). Sagittal, vertical, and transverse malocclusion features were retrieved and gathered. RESULTS: Eleven studies were selected from 434 titles. Two studies showed a high risk of bias, eight low and one moderate. Thirteen thousand two hundred seventy-one individuals were investigated from early childhood to late adulthood. Most studies were sampled from universities and dental (nonorthodontic) clinics. The pooled malocclusion prevalence was 56% for Class I (95% confidence interval (CI): 44-68%), 31% for Class II (CI: 6-42%), and 11% for Class III (CI: 21-37%). The other common types of malocclusions were crowding (41%, CI: 18-65%), overjet (34%, CI: 21-50%), negative overjet (13%, CI: 7-20%), and crossbite (11%, CI: 7-15%). Additionally, there was no significant difference in Class I (relative risk [RR] = 1.00, [0.96-1.05]), Class II ([RR] = 0.97, [0.92-1.03]), and Class III ([RR] = 1.08, [0.96-1.225]) malocclusion by gender. CONCLUSIONS: This study showed Class I malocclusion has a high prevalence among the Turkish population followed by Class II and Class III malocclusions. In addition, crowding and overjet were the most prevalent malocclusions among Turkish individuals. There were no significant differences in the prevalence of malocclusions between males and females.

How much can we rely on artificial intelligence chatbots such as the ChatGPT software program to assist with scientific writing?

STATEMENT OF PROBLEM: Use of the ChatGPT software program by authors raises many questions, primarily regarding egregious issues such as plagiarism. Nevertheless, little is known about the extent to which artificial intelligence (AI) models can produce high-quality research publications and advance and shape the direction of a research topic. PURPOSE: The purpose of this study was to determine how well the ChatGPT software program, a writing tool powered by AI, could respond to questions about scientific or research writing and generate accurate references with academic examples. MATERIAL AND METHODS: Questions were made for the ChatGPT software program to locate an abstract containing a particular keyword in the Journal of Prosthetic Dentistry (JPD). Then, whether the resulting articles existed or were published was determined. Questions were made for the algorithm 5 times to locate 5 JPD articles containing 2 specific keywords, bringing the total number of articles to 25. The process was repeated twice, each time with a different set of keywords, and the ChatGPT software program provided a total of 75 articles. The search was conducted at various times between April 1 and 4, 2023. Finally, 2 authors independently searched the JPD website and Google Scholar to determine whether the articles provided by the ChatGPT software program existed. RESULTS: When the author tested the ChatGPT software program's ability to locate articles in the JPD and Google Scholar using a set of keywords, the results did not match the papers that the ChatGPT software program had generated with the help of the AI tool. Consequently, all 75 articles provided by the ChatGPT software program were not accurately located in the JPD or Google Scholar databases and had to be added manually to ensure the accuracy of the relevant references. CONCLUSIONS: Researchers and academic scholars must be cautious when using the ChatGPT software program because AI-generated content cannot provide or analyze the same information as an author or researcher. In addition, the results indicated that writing credit or references to such content or references in prestigious academic journals is not yet appropriate. At this time, scientific writing is only valid when performed manually by researchers.

The Effect of Nanofilled Resin-Base Coating on the Mechanical and Physical Properties of Resin Composites.

OBJECTIVE: The aim of this study was to evaluate the effects of a nanofilled resin-base coating on the physical and mechanical properties of four different types of resin composites. MATERIALS AND METHODS: Disc-shaped specimens of four resin composites (Aura bulk fil [SDI], IPS Impress Direct [Ivoclar], Filtek Z250, and Filtek P60 [3M/ESPE]) were prepared and divided in two groups: either uncoated or coated with G-coat plus (n = 10). The specimens were obtained and stored in distilled water (DW) at 37°C. For water sorption and solubility, the specimens were stored for 60 days in DW and measured according to ISO 4049 instructions. The specimens were subjected to color change (ΔE), microhardness, and flexural strength (FS) measurements after 24 hours and 60 days of immersion in DW. STATISTICAL ANALYSIS: ANOVA, post hoc Tukey's, and independent t-tests were used to analyze data. RESULTS: Mean solubility values of coated groups were significantly lower than that of uncoated ones (p < 0.001). No significant differences were detected between neither water sorption nor FS of coated and uncoated groups (p = 0.502 and p = 0.510, respectively). For all materials except IPS Empress, the mean values for ΔE were not statistically different between coated and uncoated groups, and mean microhardness values of the uncoated groups were significantly greater than the coated groups (p < 0.001). CONCLUSION: Coating resin composites improved water sorption and solubility but increased ΔE, decreased microhardness, and had no significant effect on FS in the long run.