The concept of machines learning and acting like humans is what is meant by the phrase “artificial intelligence” (AI). Several branches of dentistry are increasingly relying on artificial intelligence (AI) tools. The literature usually focuses on AI models. These AI models have been used to detect and diagnose a wide range of conditions, including, but not limited to, dental caries, vertical root fractures, apical lesions, diseases of the salivary glands, maxillary sinusitis, maxillofacial cysts, cervical lymph node metastasis, osteoporosis, cancerous lesions, alveolar bone loss, the need for orthodontic extractions or treatments, cephalometric analysis, age and gender determination, and more. The primary contemporary applications of AI in the dental field are in undergraduate teaching and research. Before these methods can be used in everyday dentistry, however, the underlying technology and user interfaces need to be refined.

In recent years, research has shown that oxidative stress plays a significant role in chronic inflammatory conditions. The alteration of the oxidant/antioxidant balance leads to the appearance of free radicals, important molecules involved in both diabetes mellitus and periodontal disease. Diabetes is considered to be one of the major risk factors of periodontal disease and the inflammation characterizing this condition is associated with oxidative stress, implicitly resulting in oxidative damage to DNA. 8-Hydroxydeoxyguanosine (8-OHdG) is the most common stable product of oxidative DNA damage caused by reactive oxygen species, and its levels have been reported to increase in body fluids and tissues during inflammatory conditions. 8-OHdG emerges as a pivotal biomarker for assessing oxidative DNA damage, demonstrating its relevance across diverse health conditions, including neurodegenerative disorders, cancers, inflammatory conditions, and periodontal disease. Continued research in this field is crucial for developing more precise treatments and understanding the detailed link between oxidative stress and the progression of periodontitis. The use of the 8-OHdG biomarker in assessing and managing chronic periodontitis is an area of increased interest in dental research, with the potential to provide crucial information for diagnosis and treatment.

Introduction: Salivary biomarkers have been extensively studied in relation to oral disease, such as periodontal disease, oral cancer, and dental caries, as well as systemic conditions including diabetes, cardiovascular diseases, and neurological disorders. Literature Review: A systematic literature review was conducted, analyzing recent advancements in salivary biomarker research. Databases such as PubMed, Scopus, and Web of Science were searched for relevant studies published in the last decade. The selection criteria included studies focusing on the identification, validation, and clinical application of salivary biomarkers in diagnosing oral and systemic diseases. Various detection techniques, including enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), mass spectrometry, and biosensor technologies, were reviewed to assess their effectiveness in biomarker analysis. Specific biomarkers, such as inflammatory cytokines, oxidative stress markers, and microRNAs, have been identified as reliable indicators of disease progression. Current Trends and Future Perspectives: Advances in proteomics, genomics, and metabolomics have significantly enhanced the ability to analyze salivary biomarkers with high sensitivity and specificity. Despite the promising findings, challenges remain in standardizing sample collection, processing, and analysis to ensure reproducibility and clinical applicability. Conclusions: Future research should focus on developing point-of-care diagnostic tools and integrating artificial intelligence to improve the predictive accuracy of salivary biomarkers.

By correctly repositioning teeth, orthodontic therapy improves both the function and appearance of an occlusion. The relationship between teeth and the tissues that surround and support them significantly influences these alterations. With ever more adults seeking orthodontic care, orthodontists are increasingly seeing patients with periodontal issues. Concerns about the patient’s appearance, such as uneven gingival margins or functional issues caused by inflammatory periodontal diseases, should be accounted for when designing orthodontic treatment plans. Furthermore, orthodontics may increase the chances of saving and recovering a degraded dentition in cases of severe periodontitis. Today, general dentists, dontists, and orthodontists play integrative roles that enable them to achieve the best possible results for their patients. This review will improve the results of interdisciplinary treatments and increase cooperation between dental specialists by drawing attention to the essential connection between orthodontics and periodontics in regular clinical practice.

Placing antimicrobial treatments directly in periodontal pockets is an example of the local administration of antimicrobial drugs to treat periodontitis. This method of therapy is advantageous since the drug concentration after application far surpasses the minimum inhibitory concentration (MIC) and lasts for a number of weeks. As a result, numerous local drug delivery systems (LDDSs) utilizing various antibiotics or antiseptics have been created. There is constant effort to develop novel formulations for the localized administration of periodontitis treatments, some of which have failed to show any efficacy while others show promise. Thus, future research should focus on the way LDDSs can be personalized in order to optimize future clinical protocols in periodontal therapy.

Artificial intelligence (AI) is increasingly used in healthcare, including dental and periodontal diagnostics, due to its ability to analyze complex datasets with speed and precision. Backgrounds and Objectives: This study aimed to evaluate the reliability of AI-assisted dental–periodontal diagnoses compared to diagnoses made by senior specialists, specialists, and general dentists. Material and Methods: A comparative study was conducted involving 60 practitioners divided into three groups—general dentists, specialists, and senior specialists—along with an AI diagnostic system (Planmeca Romexis 6.4.7.software). Participants evaluated six high-quality panoramic radiographic images representing various dental and periodontal conditions. Diagnoses were compared against a reference “gold standard” validated by a dental imaging expert and senior clinician. A statistical analysis was performed using SPSS 26.0, applying chi-square tests, ANOVA, and Bonferroni correction to ensure robust results. Results: AI’s consistency in identifying subtle conditions was comparable to that of senior specialists, while general dentists showed greater variability in their evaluations. The key findings revealed that AI and senior specialists consistently demonstrated the highest performance in detecting attachment loss and alveolar bone loss, with AI achieving a mean score of 6.12 in identifying teeth with attachment loss, compared to 5.43 for senior specialists, 4.58 for specialists, and 3.65 for general dentists. The ANOVA highlighted statistically significant differences between groups, particularly in the detection of attachment loss on the maxillary arch (F = 3.820, p = 0.014). Additionally, AI showed high consistency in detecting alveolar bone loss, with comparable performance to senior specialists. Conclusions: AI systems exhibit significant potential as reliable tools for dental and periodontal assessment, complementing the expertise of human practitioners. However, further validation in clinical settings is necessary to address limitations such as algorithmic bias and atypical cases. AI integration in dentistry can enhance diagnostic precision and patient outcomes while reducing variability in clinical assessments.