Advancements in artificial intelligence (AI) have significantly impacted the field of dentistry, particularly in diagnostic imaging for caries detection. This review critically examines the current state of AI applications in caries detection, focusing on the performance and accuracy of various AI techniques. We evaluated 40 studies from the past 23 years, carefully selected for their relevance and quality. Our analysis highlights the potential of AI, especially convolutional neural networks (CNNs), to improve diagnostic accuracy and efficiency in detecting dental caries. The findings underscore the transformative potential of AI in clinical dental practice.

AI technology presents a variety of benefits and challenges for educators. To investigate whether ChatGPT and Google Bard (now is named Gemini) are valuable resources for generating multiple-choice questions for educators of dental caries. A book on dental caries was used. Sixteen paragraphs were extracted by an expert consultant based on applicability and potential for developing multiple-choice questions. ChatGPT and Bard language models were used to produce multiple-choice questions based on this input, and 64 questions were generated. Three dental specialists assessed the relevance, accuracy, and complexity of the generated questions. The questions were qualitatively evaluated using cognitive learning objectives and item writing flaws. Paired sample t-tests and two-way analysis of variance (ANOVA) were used to compare the generated multiple-choice questions and answers between ChatGPT and Bard. There were no significant differences between the questions generated by ChatGPT and Bard. Moreover, the analysis of variance found no significant differences in question quality. Bard-generated questions tended to have higher cognitive levels than those of ChatGPT. Format error was predominant in ChatGPT-generated questions. Finally, Bard exhibited more absolute terms than ChatGPT. ChatGPT and Bard could generate questions related to dental caries, mainly at the cognitive level of knowledge and comprehension. Language models are crucial for generating subject-specific questions used in quizzes, tests, and education. By using these models, educators can save time and focus on lesson preparation and student engagement instead of solely focusing on assessment creation. Additionally, language models are adept at generating numerous questions, making them particularly valuable for large-scale exams. However, educators must carefully review and adapt the questions to ensure they align with their learning goals.

Objective: Access to dental care is a key factor influencing oral health outcomes. Individuals with special healthcare needs are at risk of not having access to dental care services which they need to maintain their oral health. This study assessed the magnitude of this problem and identified barriers responsible for the difficulties in accessing dental care in Qatif, Saudi Arabia, as reported by caregivers of individuals with special healthcare needs. Methods: This cross-sectional study collected data using a self-administered survey questionnaire from caregivers of individuals with special healthcare needs across 11 centers (eight special needs centers and three schools) in Qatif, Eastern Province of Saudi Arabia, between February and April 2019. Results: A total of 186 caregivers participated in the study, 102 (54.8%) of whom reported difficulties in getting access to dental care. The key barriers included lack of time on the part of caregivers (60.8%), unsuitable clinic environment (53.9%), difficulties with transportation (51.9%), medical/health status of the individual with special healthcare needs (51.0%), and geographically distant dental clinics (51.0%). Caregiver demographics (age, gender, and educational level) had no significant influence on the difficulties reported by caregivers in getting access to dental care for individuals with special healthcare needs (p> 0.05). Conclusion: A large proportion of caregivers in Qatif, Saudi Arabia, experience difficulties with access to dental care services for individuals with special healthcare needs. The most common barriers are physical accessibility of dental facilities (for individuals with special healthcare needs), affordability, and lack of skills and knowledge of dental care providers.

This systematic review aimed to study the outcomes of CAD-CAM (milled and 3D-printed) interim dental prostheses when compared to conventional ones. The focused question of “In natural teeth, what are the outcomes of CAD-CAM interim FDPs compared to the conventionally-manufactured ones regarding marginal fit, mechanical properties, esthetics, and color stability” was formulated. The systematic search was conducted electronically in the PubMed/MEDLINE, CENTRAL, EMBASE, Web of Science, New York Academy of Medicine Grey Literature Report, and Google Scholar databases by using the MeSH keywords and keywords associated with the focused question and limiting articles to those published between 2000 and 2022. A manual search was conducted in selected dental journals. The results were analyzed qualitatively and are presented in table format. Of the included studies, 18 studies were in vitro and 1 was a randomized clinical trial. Of the eight studies analyzing the mechanical properties, five studies favored the milled interim restorations, one study favored both 3D-printed and milled interim restorations, and two studies reported better mechanical properties in conventional interim restorations. Among four studies evaluating the marginal discrepancies, two studies favored the marginal fit in milled interim restorations, one study reported a better marginal fit in both milled and 3D-printed interim restorations, and one study found conventional interim restorations have a better marginal fit and smaller marginal discrepancy when compared to both milled and 3D-printed restorations. Among five studies that evaluated both the mechanical properties and marginal fit, 1 study favored 3D-printed interim restorations and four studies favored milled interim restorations over the conventional ones. Two studies analyzing the esthetics outcomes demonstrated better results with milled interim restorations compared to conventional and 3D-printed interim restorations in terms of their color stabilities. The risk of bias was low for all the studies reviewed. The high level of heterogeneity within the studies excluded meta-analysis. Most of the studies favored the milled interim restorations over the 3D-printed and conventional restorations. The results suggested that milled interim restorations offer a better marginal fit, higher mechanical properties, and better esthetic outcomes in terms of color stabilities.

Purpose: To describe and discuss the benefits and drawbacks of various dental caries diagnostic techniques, including the use of intraoral scanners for caries diagnosis based on near‐infrared imaging (NIR) technology.Material and Methods: A MEDLINE search from 1980–2023 focused on dental caries diagnostic techniques, emphasizing intraoral scanners using NIR technology. Alternative caries detection methods were also evaluated for their advantages and limitations, enabling a comparison with NIR. The review included traditional caries tools, the latest detection methods, and NIR's role in intraoral scanners, drawing from case reports and both in vivo and in vitro studies. Keywords like “caries detection,” “intraoral scanners,” and “Near Infrared Imaging (NIRI)” guided the search. After screening titles and abstracts for relevance, full texts with valuable insights were thoroughly analyzed. The data was grouped into three: traditional diagnostics, advanced digital methods, and intraoral scanner‐based detection.Results: This comprehensive narrative review described and discussed the current state of dental caries diagnostic methods, given the insufficient number of clinical investigations suitable for a systematic review. Traditional caries diagnosis techniques have shown variable accuracy dependent on a dentist's experience and the potential over‐removal of healthy tooth structures. Intraoral scanners have emerged as a novel caries detection method, because of their integration of NIR technology. Various studies have confirmed the efficacy of NIR in detecting interproximal caries and in the early diagnosis of non‐cavitated caries. Specifically, intraoral scanners have demonstrated promising results, proving comparable to established diagnostic methods like bitewing radiography. Nevertheless, while the integration of NIR into intraoral scanners seems promising, the technology still faces challenges, notably its accuracy in detecting secondary and subgingival cavities. However, with anticipated integrations of AI, NIR in intraoral scanners could revolutionize early caries detection.Conclusions: Intraoral scanners with NIR technology offer non‐destructive imaging, real‐time lesion visuals, and enhanced patient communication. Although comparable to bitewing radiography in some studies, a universally accepted diagnostic tool is lacking. Future research should compare them with existing methods, focusing on clinical outcomes, cost‐effectiveness, and patient acceptance.

Objectives: This review aims to explore the applications of artificial intelligence (AI) in prosthodontics and implant dentistry, focusing on its performance outcomes and associated ethical concerns. Materials and Methods: Following the PRISMA guidelines, a search was conducted across databases such as PubMed, Medline, Web of Science, and Scopus. Studies published between January 2022 and May 2024, in English, were considered. The Population (P) included patients or extracted teeth with AI applications in prosthodontics and implant dentistry; the Intervention (I) was AI-based tools; the Comparison (C) was traditional methods, and the Outcome (O) involved AI performance outcomes and ethical considerations. The Newcastle–Ottawa Scale was used to assess the quality and risk of bias in the studies. Results: Out of 3420 initially identified articles, 18 met the inclusion criteria for AI applications in prosthodontics and implant dentistry. The review highlighted AI’s significant role in improving diagnostic accuracy, treatment planning, and prosthesis design. AI models demonstrated high accuracy in classifying dental implants and predicting implant outcomes, although limitations were noted in data diversity and model generalizability. Regarding ethical issues, five studies identified concerns such as data privacy, system bias, and the potential replacement of human roles by AI. While patients generally viewed AI positively, dental professionals expressed hesitancy due to a lack of familiarity and regulatory guidelines, highlighting the need for better education and ethical frameworks. Conclusions: AI has the potential to revolutionize prosthodontics and implant dentistry by enhancing treatment accuracy and efficiency. However, there is a pressing need to address ethical issues through comprehensive training and the development of regulatory frameworks. Future research should focus on broadening AI applications and addressing the identified ethical concerns.