Artificial intelligence, rooted in the information technology sector, stands as a transformative force driven by sophisticated software mechanisms that confer numerous benefits across a wide array of societal domains. Among these, its integration into the healthcare sector, including dentistry, has proven to be a source of considerable success. The impact is particularly noteworthy in the realm of preventive healthcare, where AI demonstrates substantial potential for advancing early diagnosis, ultimately resulting in elevated treatment efficacy. The exploration of diverse use cases reveals a promising landscape where AI not only facilitates precision in diagnostics but also plays a pivotal role in optimizing treatment outcomes. Through its analytical capabilities and pattern recognition, AI contributes to a more nuanced and accurate early detection of dental issues, thereby empowering dental professionals to intervene at the earliest stages. The implications extend beyond diagnostic accuracy, encompassing tangible improvements in the overall patient experience. By leveraging AI, treatment durations can be streamlined, leading to more efficient and effective interventions. This not only translates to enhanced patient comfort but also contributes to the professional satisfaction of dental practitioners who can deliver high-quality care with greater precision. Furthermore, the synthesis of AI into dental practices introduces a dynamic paradigm shift, fostering a synergy between technology and human expertise. As algorithms evolve and learn from vast datasets, they augment the diagnostic acumen of dental professionals, providing valuable insights that may have been challenging to discern through traditional means alone. This narrative review explores the diverse applications of artificial intelligence in dentistry, emphasizing its transformative potential. From early diagnosis to treatment optimization, the integration of AI not only enhances clinical outcomes but also fosters a more patient-centric and efficient approach to dental care. The evolving collaboration between AI and dental practitioners is set to redefine oral healthcare, paving the way for a future where precision and efficiency converge to elevate the standards of dental medicine.

Background: Numerous biomaterials used in prosthodontics have demonstrated dramatically improved performances after having their scales decreased by nanotechnology from micron to nano size. Contrarily, numerous nanocomposites made of nanomaterials and conventional metals, ceramics, resins, or other matrix materials have been extensively used in prosthodontics because the addition of the nanomaterials significantly improved the materials' properties, including modulus elasticity, surface hardness, polymerization shrinkage, and filler loading. Aim of the Study: In this paper, the most recent advancements in research on the prosthodontic applications of nanometals, nanoceramics, nano resins, and other nanomaterials were reviewed. This review not only provides a detailed description of the most recent related investigations, but also, hopefully, serves as an important elicitation for future studies in this area. Conclusion: Nanomaterials have proven fundamental to the development of basic science and clinical technology in prosthodontics. It demonstrates how the prosthodontic materials' many properties, including modulus of elasticity, surface hardness, polymerization shrinkage, and filler loading, can be significantly improved after their scales were reduced from micron to nano size by nanotechnology.

Background: Early childhood caries is a biofilm-dependent, critical, widespread health problem in both developing and industrialized countries. The interleukin-6 (IL-6) has several roles in the body's response to inflammation and tissue damage. TNF-α is a pleiotropic cytokine that acts as a pro-inflammatory cytokine. Aim of Study: The purpose of this research was to assess the salivary concentrations of TNF-α and IL-6 in children with dental caries and study the correlation between cytokines and DMFs index. Materials and Methods: Eighty children aged four and five took part in the study. Forty children with ECC served as the research group, whereas 40 caries-free children of the same age and gender served as the control group. Clinical parameters were assessed after an oral examination and measurement. All participants had saliva samples obtained. IL-6 and TNF-α concentrations in saliva were estimated using an ELISA test. Results: The current study found a significant elevation (P<0.05) in mean levels of TNF-α and IL-6 among children with ECC in comparison to caries free children. Moreover, there is significant positive correlation between IL-6 and TNF-α levels in caries children. Conclusion: Based on these results, it seems likely that elevated levels of TNF-α and IL-6 are involved in ECC.