Hasil untuk "Dentistry"

I. Yin, Jing Zhang, I. Zhao et al.

Abstract Nanotechnology has recently emerged as a rapidly growing field with numerous biomedical science applications. At the same time, silver has been adopted as an antimicrobial material and disinfectant that is relatively free of adverse effects. Silver nanoparticles possess a broad spectrum of antibacterial, antifungal and antiviral properties. Silver nanoparticles have the ability to penetrate bacterial cell walls, changing the structure of cell membranes and even resulting in cell death. Their efficacy is due not only to their nanoscale size but also to their large ratio of surface area to volume. They can increase the permeability of cell membranes, produce reactive oxygen species, and interrupt replication of deoxyribonucleic acid by releasing silver ions. Researchers have studied silver nanoparticles as antimicrobial agents in dentistry. For instance, silver nanoparticles can be incorporated into acrylic resins for fabrication of removable dentures in prosthetic treatment, composite resin in restorative treatment, irrigating solution and obturation material in endodontic treatment, adhesive materials in orthodontic treatment, membrane for guided tissue regeneration in periodontal treatment, and titanium coating in dental implant treatment. Although not all authorities have acknowledged the safety of silver nanoparticles, no systemic toxicity of ingested silver nanoparticles has been reported. A broad concern is their potential hazard if they are released into the environment. However, the interaction of nanoparticles with toxic materials and organic compounds can either increase or reduce their toxicity. This paper provides an overview of the antibacterial use of silver nanoparticles in dentistry, highlighting their antibacterial mechanism, potential applications and safety in clinical treatment.

F. Schwendicke, W. Samek, J. Krois

The term “artificial intelligence” (AI) refers to the idea of machines being capable of performing human tasks. A subdomain of AI is machine learning (ML), which “learns” intrinsic statistical patterns in data to eventually cast predictions on unseen data. Deep learning is a ML technique using multi-layer mathematical operations for learning and inferring on complex data like imagery. This succinct narrative review describes the application, limitations and possible future of AI-based dental diagnostics, treatment planning, and conduct, for example, image analysis, prediction making, record keeping, as well as dental research and discovery. AI-based applications will streamline care, relieving the dental workforce from laborious routine tasks, increasing health at lower costs for a broader population, and eventually facilitate personalized, predictive, preventive, and participatory dentistry. However, AI solutions have not by large entered routine dental practice, mainly due to 1) limited data availability, accessibility, structure, and comprehensiveness, 2) lacking methodological rigor and standards in their development, 3) and practical questions around the value and usefulness of these solutions, but also ethics and responsibility. Any AI application in dentistry should demonstrate tangible value by, for example, improving access to and quality of care, increasing efficiency and safety of services, empowering and enabling patients, supporting medical research, or increasing sustainability. Individual privacy, rights, and autonomy need to be put front and center; a shift from centralized to distributed/federated learning may address this while improving scalability and robustness. Lastly, trustworthiness into, and generalizability of, dental AI solutions need to be guaranteed; the implementation of continuous human oversight and standards grounded in evidence-based dentistry should be expected. Methods to visualize, interpret, and explain the logic behind AI solutions will contribute (“explainable AI”). Dental education will need to accompany the introduction of clinical AI solutions by fostering digital literacy in the future dental workforce.

Rusin Zhao, Ruijia Yang, P. Cooper et al.

After tooth loss, bone resorption is irreversible, leaving the area without adequate bone volume for successful implant treatment. Bone grafting is the only solution to reverse dental bone loss and is a well-accepted procedure required in one in every four dental implants. Research and development in materials, design and fabrication technologies have expanded over the years to achieve successful and long-lasting dental implants for tooth substitution. This review will critically present the various dental bone graft and substitute materials that have been used to achieve a successful dental implant. The article also reviews the properties of dental bone grafts and various dental bone substitutes that have been studied or are currently available commercially. The various classifications of bone grafts and substitutes, including natural and synthetic materials, are critically presented, and available commercial products in each category are discussed. Different bone substitute materials, including metals, ceramics, polymers, or their combinations, and their chemical, physical, and biocompatibility properties are explored. Limitations of the available materials are presented, and areas which require further research and development are highlighted. Tissue engineering hybrid constructions with enhanced bone regeneration ability, such as cell-based or growth factor-based bone substitutes, are discussed as an emerging area of development.

Ziying Ge, Luyi Yang, Jiajia Xia et al.

Since its emergence in December 2019, corona virus disease 2019 (COVID-19) has impacted several countries, affecting more than 90 thousand patients and making it a global public threat. The routes of transmission are direct contact, and droplet and possible aerosol transmissions. Due to the unique nature of dentistry, most dental procedures generate significant amounts of droplets and aerosols, posing potential risks of infection transmission. Understanding the significance of aerosol transmission and its implications in dentistry can facilitate the identification and correction of negligence in daily dental practice. In addition to the standard precautions, some special precautions that should be implemented during an outbreak have been raised in this review.

R. Paravina, M. M. Pérez, R. Ghinea

OBJECTIVE The objective was to provide a literature review on perceptibility and acceptability thresholds in dentistry and corresponding recommendations. OVERVIEW A literature review on visual thresholds included findings on the judgments of the color and appearance of tooth-, gingiva- and skin-colored restorative dental materials. Discrepancies in study design contributed to inconsistencies in the research findings of some studies. These differences are related to (a) number of observers and inclusion criteria, (b) specimen number and size, (c) color measurement instrument and the setup and formulas used, (d) psychophysical experiment, (e) data processing (fitting method), and (f) % perceptibility or % acceptability values. A straightforward, consistent and practical model for the clinical and research application and interpretation of visual thresholds and recommended protocols for threshold research were provided. CONCLUSIONS Visual thresholds are of paramount importance as a quality control tool and guide the evaluation and selection of dental materials and their clinical performance. Although clinical shade matching conditions and method are rarely controlled, research on visual thresholds, especially when aiming to set standards for the profession, must be carefully planned and executed. CLINICAL SIGNIFICANCE Perceptibility and acceptability thresholds define visual match or mismatch of color, translucency, and whiteness in dentistry. Clinical and research findings cannot be fully interpreted in terms of real-life relevance without comparison with perceptibility and acceptability tolerances.

Yueyi Tian, ChunXu Chen, Xiaotong Xu et al.

Three-dimensional (3D) printing technologies are advanced manufacturing technologies based on computer-aided design digital models to create personalized 3D objects automatically. They have been widely used in the industry, design, engineering, and manufacturing fields for nearly 30 years. Three-dimensional printing has many advantages in process engineering, with applications in dentistry ranging from the field of prosthodontics, oral and maxillofacial surgery, and oral implantology to orthodontics, endodontics, and periodontology. This review provides a practical and scientific overview of 3D printing technologies. First, it introduces current 3D printing technologies, including powder bed fusion, photopolymerization molding, and fused deposition modeling. Additionally, it introduces various factors affecting 3D printing metrics, such as mechanical properties and accuracy. The final section presents a summary of the clinical applications of 3D printing in dentistry, including manufacturing working models and main applications in the fields of prosthodontics, oral and maxillofacial surgery, and oral implantology. The 3D printing technologies have the advantages of high material utilization and the ability to manufacture a single complex geometry; nevertheless, they have the disadvantages of high cost and time-consuming postprocessing. The development of new materials and technologies will be the future trend of 3D printing in dentistry, and there is no denying that 3D printing will have a bright future.

S. Khanagar, A. Al-Ehaideb, P. Maganur et al.

Background/purpose Artificial intelligence (AI) has made deep inroads into dentistry in the last few years. The aim of this systematic review was to identify the development of AI applications that are widely employed in dentistry and evaluate their performance in terms of diagnosis, clinical decision-making, and predicting the prognosis of the treatment. Materials and methods The literature for this paper was identified and selected by performing a thorough search in the electronic data bases like PubMed, Medline, Embase, Cochrane, Google scholar, Scopus, Web of science, and Saudi digital library published over the past two decades (January 2000–March 15, 2020).After applying inclusion and exclusion criteria, 43 articles were read in full and critically analyzed. Quality analysis was performed using QUADAS-2. Results AI technologies are widely implemented in a wide range of dentistry specialties. Most of the documented work is focused on AI models that rely on convolutional neural networks (CNNs) and artificial neural networks (ANNs). These AI models have been used in detection and diagnosis of dental caries, vertical root fractures, apical lesions, salivary gland diseases, maxillary sinusitis, maxillofacial cysts, cervical lymph nodes metastasis, osteoporosis, cancerous lesions, alveolar bone loss, predicting orthodontic extractions, need for orthodontic treatments, cephalometric analysis, age and gender determination. Conclusion These studies indicate that the performance of an AI based automated system is excellent. They mimic the precision and accuracy of trained specialists, in some studies it was found that these systems were even able to outmatch dental specialists in terms of performance and accuracy.

Hanyao Huang, Ou Zheng, Dongdong Wang et al.

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.

Frank Poppolo Deus, Aviv Ouanounou

Objectives The aim of this work was to review the current uses of chlorhexidine (CHX) in dentistry based on its mechanism of action, whilst highlighting the most effective protocols that render the highest clinical efficacy whilst limiting adverse drug reactions. Methods A literature search was conducted using the key words chlorhexidine, mechanism of action, adverse effects, and dentistry using databases in the University of Toronto library system. The titles and abstracts were read, and relevant articles were selected. Results A total of 1100 publications were identified, 100 were investigated, and 67 of them were used. Out of the 67 selected articles, 12 were reviews on CHX; 5 articles focussed on CHX gels; 13 focussed on CHX mouthwashes; 8 focussed on CHX products; 13 discussed adverse effects associated with CHX; 13 focussed on periodontal pathology and treatment; 6 focussed on implant periodontal and dental surgeries; 7 evaluated effects on caries; 6 looked at the mechanisms of action; and 12 focussed on the antibacterial and antimicrobial impact on the oral biome. There were multiple areas of overlap amongst the articles, and results showed that CHX provides different uses, but mainly as an adjunct to various treatments. Mouthwash was the most superior medium when used in short time spans when mechanical prophylaxis was not possible for the prevention of gingivitis and maintenance of oral hygiene. CHX products are often used in periodontics, post–oral surgical procedures, and as a prophylaxis for multiple invasive procedures with minimal adverse effects. Tooth staining was the most negative adverse effect reported by patients. Conclusions CHX's antimicrobial properties make it an ideal prophylactic when mechanical debridement is not possible. CHX mouthwash appears to be more effective compared to gels. Concentrations of 0.12% to 0.2% are recommended; any mouthwash with concentrations above 0.2% will unnecessarily increase the unwanted side effects. CHX is useful amongst various areas of dentistry including oral surgery, periodontics, and even general dentistry. For long-term treatments, especially in periodontitis patients (stage I-III) undergoing nonsurgical treatments, CHX chips are recommended. CHX chips are also recommended as an adjunct to implant debridement in patients with peri-implant mucositis and peri-implantitis over CHX mouthwash and gels.

Vidhi H. Sheth, N. Shah, R. Jain et al.

STATEMENT OF PROBLEM Systematic reviews and meta-analyses have emerged as forerunners of evidence-based dentistry, but assessing the quality of the available research is essential so that it can be applied to clinical practice. While a wide variety of risk-of-bias tools are available, each specifically developed for different study designs, a comprehensive tool exclusively framed to assess the quality of in vitro dental studies is lacking. PURPOSE The purpose of this study was to develop and validate a tool to evaluate the quality and risk of bias of in vitro dental studies. MATERIAL AND METHODS A Delphi panel was established to conceptualize and develop the Quality Assessment Tool For In Vitro Studies (QUIN Tool). The tool was evaluated by using content validity and reliability testing methods. RESULTS The QUIN Tool includes 12 points along with scoring and grading options to allow clinicians to evaluate the quality of in vitro studies. This tool shows good content validity and reliability. CONCLUSIONS The QUIN Tool is user-friendly, efficient, and effective for evaluating the risk of bias of in vitro studies.

Lakshman Samaranayake, N. Tuygunov, F. Schwendicke et al.

Artificial intelligence (AI) holds immense promise in revolutionising dentistry, spanning, diagnostics, treatment planning and educational realms. This narrative review, in two parts, explores the fundamentals and the multifaceted potential of AI in dentistry. The current article explores the profound impact of AI in dentistry, encompassing diagnostic tools, treatment planning, and patient care. The Part 2 of the article delves into the potential of AI in patient education, ethics and the FDI communique on AI in dentistry. The review begins by elucidating the historical context of AI, outlining its recent widespread use in various sectors, including medicine and dentistry. The narrative delves into the fundamental concepts of AI, which entails developing machines capable of executing tasks that typically necessitate human intellect. In the biomedical realm, AI has evolved from exploring computational models to constructing systems for clinical data processing and interpretation, aiming to enhance medical/dental decision-making. The discussion delves into the pivotal role of AI models in dentistry, such as Large Language Models (LLM), Large Vision Models (LVM), and Multimodality Models (MM), revolutionizing processes from clinical documentation to treatment planning. The narrative extends to the applications of AI in dental specialties such as periodontics, endodontics, oral medicine and pathology, restorative dentistry, prosthodontics, paediatric dentistry, forensic odontology, oral and maxillofacial surgery, orthodontics, and orofacial pain management. AI's role in improving treatment outcomes, diagnostic accuracy, and decision-making processes is evident across these specialties, showcasing its potential in transforming dental care. The review concludes by highlighting the need for continued validation, interdisciplinary collaboration, and regulatory frameworks to ensure the seamless integration of AI into dentistry, paving the way for enhanced patient outcomes and evidence-based practice in the field.

N. Tuygunov, Lakshman Samaranayake, Z. Khurshid et al.

In the final part of this two part article on artificial intelligence (AI) in dentistry we review its transformative role, focusing on AI in dental education, patient communications, challenges of integration, strategies to overcome barriers, ethical considerations, and finally, the recently released International Dental Federation (FDI) Communique (white paper) on AI in Dentistry. AI in dental education is highlighted for its potential in enhancing theoretical and practical dimensions, including patient telemonitoring and virtual training ecosystems. Challenges of AI integration in dentistry are outlined, such as data availability, bias, and human accountability. Strategies to overcome these challenges include promoting AI literacy, establishing regulations, and focusing on specific AI implementations. Ethical considerations in AI integration within dentistry, such as patient privacy and algorithm bias, are emphasized. The need for clear guidelines and ongoing evaluation of AI systems is crucial. The FDI White Paper on AI in Dentistry provides insights into the significance of AI in oral care, dental education, and research, along with standards for governance. It discusses AI's impact on individual patients, community health, dental education, and research. The paper addresses biases, limited generalizability, accessibility, and regulatory requirements for AI in dental practice. In conclusion, AI plays a significant role in modern dental care, offering benefits in diagnosis, treatment planning, and decision-making. While facing challenges, strategic initiatives focusing on AI literacy, regulations, and targeted implementations can help overcome barriers and maximize the potential of AI in dentistry. Ethical considerations and ongoing evaluation are essential for ensuring responsible, effective and efficacious deployment of AI technologies in dental ecosystem.

Kostis Giannakopoulos, Argyro Kavadella, Anas Aaqel Salim et al.

Background The increasing application of generative artificial intelligence large language models (LLMs) in various fields, including dentistry, raises questions about their accuracy. Objective This study aims to comparatively evaluate the answers provided by 4 LLMs, namely Bard (Google LLC), ChatGPT-3.5 and ChatGPT-4 (OpenAI), and Bing Chat (Microsoft Corp), to clinically relevant questions from the field of dentistry. Methods The LLMs were queried with 20 open-type, clinical dentistry–related questions from different disciplines, developed by the respective faculty of the School of Dentistry, European University Cyprus. The LLMs’ answers were graded 0 (minimum) to 10 (maximum) points against strong, traditionally collected scientific evidence, such as guidelines and consensus statements, using a rubric, as if they were examination questions posed to students, by 2 experienced faculty members. The scores were statistically compared to identify the best-performing model using the Friedman and Wilcoxon tests. Moreover, the evaluators were asked to provide a qualitative evaluation of the comprehensiveness, scientific accuracy, clarity, and relevance of the LLMs’ answers. Results Overall, no statistically significant difference was detected between the scores given by the 2 evaluators; therefore, an average score was computed for every LLM. Although ChatGPT-4 statistically outperformed ChatGPT-3.5 (P=.008), Bing Chat (P=.049), and Bard (P=.045), all models occasionally exhibited inaccuracies, generality, outdated content, and a lack of source references. The evaluators noted instances where the LLMs delivered irrelevant information, vague answers, or information that was not fully accurate. Conclusions This study demonstrates that although LLMs hold promising potential as an aid in the implementation of evidence-based dentistry, their current limitations can lead to potentially harmful health care decisions if not used judiciously. Therefore, these tools should not replace the dentist’s critical thinking and in-depth understanding of the subject matter. Further research, clinical validation, and model improvements are necessary for these tools to be fully integrated into dental practice. Dental practitioners must be aware of the limitations of LLMs, as their imprudent use could potentially impact patient care. Regulatory measures should be established to oversee the use of these evolving technologies.

H. Ding, Jiamin Wu, Wuyuan Zhao et al.

Artificial Intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence. AI is not a new term, the concept of AI can be dated back to 1950. However, it has not become a practical tool until two decades ago. Owing to the rapid development of three cornerstones of current AI technology—big data (coming through digital devices), computational power, and AI algorithm—in the past two decades, AI applications have been started to provide convenience to people's lives. In dentistry, AI has been adopted in all dental disciplines, i.e., operative dentistry, periodontics, orthodontics, oral and maxillofacial surgery, and prosthodontics. The majority of the AI applications in dentistry go to the diagnosis based on radiographic or optical images, while other tasks are not as applicable as image-based tasks mainly due to the constraints of data availability, data uniformity, and computational power for handling 3D data. Evidence-based dentistry (EBD) is regarded as the gold standard for the decision-making of dental professionals, while AI machine learning (ML) models learn from human expertise. ML can be seen as another valuable tool to assist dental professionals in multiple stages of clinical cases. This review narrated the history and classification of AI, summarised AI applications in dentistry, discussed the relationship between EBD and ML, and aimed to help dental professionals to understand AI as a tool better to assist their routine work with improved efficiency.

Hind Alhaidry, Bader Fatani, Jenan O Alrayes et al.

Chat generative pre-trained transformer (ChatGPT) is an artificial intelligence chatbot that uses natural language processing that can respond to human input in a conversational manner. ChatGPT has numerous applications in the health care system including dentistry; it is used in diagnoses and for assessing disease risk and scheduling appointments. It also has a role in scientific research. In the dental field, it has provided many benefits such as detecting dental and maxillofacial abnormalities on panoramic radiographs and identifying different dental restorations. Therefore, it helps in decreasing the workload. But even with these benefits, one should take into consideration the risks and limitations of this chatbot. Few articles mentioned the use of ChatGPT in dentistry. This comprehensive review represents data collected from 66 relevant articles using PubMed and Google Scholar as databases. This review aims to discuss all relevant published articles on the use of ChatGPT in dentistry.

R. Rokhshad, Ping Zhang, Hossein Mohammad-Rahimi et al.

OBJECTIVES Artificial Intelligence has applications such as Large Language Models (LLMs), which simulate human-like conversations. The potential of LLMs in healthcare is not fully evaluated. This pilot study assessed the accuracy and consistency of chatbots and clinicians in answering common questions in pediatric dentistry. METHODS Two expert pediatric dentists developed thirty true or false questions involving different aspects of pediatric dentistry. Publicly accessible chatbots (Google Bard, ChatGPT4, ChatGPT 3.5, Llama, Sage, Claude 2 100k, Claude-instant, Claude-instant-100k, and Google Palm) were employed to answer the questions (3 independent new conversations). Three groups of clinicians (general dentists, pediatric specialists, and students; n=20/group) also answered. Responses were graded by two pediatric dentistry faculty members, along with a third independent pediatric dentist. Resulting accuracies (percentage of correct responses) were compared using analysis of variance (ANOVA), and post-hoc pairwise group comparisons were corrected using Tukey's HSD method. ACronbach's alpha was calculated to determine consistency. RESULTS Pediatric dentists were significantly more accurate (mean±SD 96.67%± 4.3%) than other clinicians and chatbots (p0.7). CONCLUSION In this pilot study, chatbots showed lower accuracy than dentists. Chatbots may not yet be recommended for clinical pediatric dentistry.

Min Jeong, Kyle Radomski, Diana Lopez et al.

Purpose. This narrative review aims to provide an overview of the mechanisms of 3D printing, the dental materials relevant to each mechanism, and the possible applications of these materials within different areas of dentistry. Methods. Subtopics within 3D printing technology in dentistry were identified and divided among five reviewers. Electronic searches of the Medline (PubMed) database were performed with the following search keywords: 3D printing, digital light processing, stereolithography, digital dentistry, dental materials, and a combination of the keywords. For this review, only studies or review papers investigating 3D printing technology for dental or medical applications were included. Due to the nature of this review, no formal evidence-based quality assessment was performed, and the search was limited to the English language without further restrictions. Results. A total of 64 articles were included. The significant applications, applied materials, limitations, and future directions of 3D printing technology were reviewed. Subtopics include the chronological evolution of 3D printing technology, the mechanisms of 3D printing technologies along with different printable materials with unique biomechanical properties, and the wide range of applications for 3D printing in dentistry. Conclusions: This review article gives an overview of the history and evolution of 3D printing technology, as well as its associated advantages and disadvantages. Current 3D printing technologies include stereolithography, digital light processing, fused deposition modeling, selective laser sintering/melting, photopolymer jetting, powder binder, and 3D laser bioprinting. The main categories of 3D printing materials are polymers, metals, and ceramics. Despite limitations in printing accuracy and quality, 3D printing technology is now able to offer us a wide variety of potential applications in different fields of dentistry, including prosthodontics, implantology, oral and maxillofacial, orthodontics, endodontics, and periodontics. Understanding the existing spectrum of 3D printing applications in dentistry will serve to further expand its use in the dental field. Three-dimensional printing technology has brought about a paradigm shift in the delivery of clinical care in medicine and dentistry. The clinical use of 3D printing has created versatile applications which streamline our digital workflow. Technological advancements have also paved the way for the integration of new dental materials into dentistry.

Maryam Ghaffari, Yi Zhu, Annie Shrestha

Artificial intelligence (AI) has been used in healthcare for decades and has the potential to revolutionize dentistry by solving multiple clinical problems and making the work of clinicians easier. In particular, the study of AI applications in periodontal disease and cariology is important because these are two major areas of concern in dental health. Periodontal disease, which affects the gums and bone surrounding the teeth, is a major cause of tooth loss in adults. Cariology, the study of dental decay, is also an important area of focus for AI research. AI algorithms can be used to analyze dental images and detect early signs of decay that may be missed by human dentists. The review first discusses the history of AI in healthcare and then highlights some of the ways technology has improved dentistry and then describe some basic AI models such as artificial neural networks (ANNs), convolutional neural networks (CNNs), and random forest. The article then delves into how AI is involved in periodontal disease, cariology, endodontics, prosthodontics, and orthodontics including classifying different types of periodontal disease, identifying areas of bone loss, determining the severity of the disease, analyzing dental images, and detecting early signs of diseases. On the other hand, the application of AI in dentistry is relatively uncommon because implementing AI technologies in dentistry presents several challenges that need to be addressed for successful implementation of AI technologies in dentistry.

Suchada Kongkiatkamon, Dinesh Rokaya, Santiphab Kengtanyakich et al.

Zirconia, a crystalline oxide of zirconium, holds good mechanical, optical, and biological properties. The metal-free restorations, mostly consisting of all-ceramic/zirconia restorations, are becoming popular restorative materials in restorative and prosthetic dentistry choices for aesthetic and biological reasons. Dental zirconia has increased over the past years producing wide varieties of zirconia for prosthetic restorations in dentistry. At present, literature is lacking on the recent zirconia biomaterials in dentistry. Currently, no article has the latest information on the various zirconia biomaterials in dentistry. Hence, the aim of this article is to present an overview of recent dental zirconia biomaterials and tends to classify the recent zirconia biomaterials in dentistry. This article is useful for dentists, dental technicians, prosthodontists, academicians, and researchers in the field of dental zirconia.

Sang J. Lee, Jessica Poon, Apissada Jindarojanakul et al.

OBJECTIVES This narrative review aimed to explore the evolution and advancements of artificial intelligence technologies, highlighting their transformative impact on healthcare, education, and specific aspects within dentistry as a field. DATA AND SOURCES Subtopics within artificial intelligence technologies in dentistry were identified and divided among four reviewers. Electronic searches were performed with search terms that included: artificial intelligence, technologies, healthcare, education, dentistry, restorative, prosthodontics, periodontics, endodontics, oral surgery, oral pathology, oral medicine, implant dentistry, dental education, dental patient care, dental practice management, and combinations of the keywords. STUDY selection: A total of 120 articles were included for review that evaluated the use of artificial intelligence technologies within the healthcare and dental field. No formal evidence-based quality assessment was performed due to the narrative nature of this review. The conducted search was limited to the English language with no other further restrictions. RESULTS The significance and applications of artificial intelligence technologies on the areas of dental education, dental patient care, and dental practice management were reviewed, along with the existing limitations and future directions of artificial intelligence in dentistry as whole. Current artificial intelligence technologies have shown promising efforts to bridge the gap between theoretical knowledge and clinical practice in dental education, as well as improved diagnostic information gathering and clinical decision-making abilities in patient care throughout various dental specialties. The integration of artificial intelligence into patient administration aspects have enabled practices to develop more efficient management workflows. CONCLUSIONS Despite the limitations that exist, the integration of artificial intelligence into the dental profession comes with numerous benefits that will continue to evolve each day. While the challenges and ethical considerations, mainly concerns about data privacy, are areas that need to be further addressed, the future of artificial intelligence in dentistry looks promising, with ongoing research aimed at overcoming current limitations and expanding artificial intelligence technologies.