The emergence of digital dentistry has led to the introduction of various three-dimensional (3D) printing materials in the market, specifically for provisional fixed restoration. This study aimed to undertake a systematic review of the published literature on the Mechanical Properties of 3D- Printed Provisional Resin Materials for crown and fixed dental prosthesis (FDP). The electronic database on PubMed/Medline was searched for relevant studies. The search retrieved articles that were published from January 2011 to March 2023. The established focus question was: “Do provisional 3D-printed materials have better mechanical properties than conventional or milled provisional materials?”. The systematically extracted data included the researcher’s name(s), publication year, evaluation method, number of samples, types of materials, and study outcome. A total of 19 studies were included in this systematic review. These studies examined different aspects of the mechanical properties of 3D-printed provisional materials. Flexural Strength and Microhardness were the frequently used mechanical testing. Furthermore, 3D-printed provisional restorations showed higher hardness, smoother surfaces, less wear volume loss, and higher wear resistance compared to either milled or conventional, or both. 3D-printed provisional resin materials appear to be a promising option for fabricating provisional crowns and FDPs.

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.