Saliva has emerged as a powerful diagnostic tool due to its non-invasive collection, straightforward storage, and ability to mirror systemic health. This narrative review explores the diagnostic potential of salivary biomarkers in detecting systemic diseases, supported by examples such as salivary proteomics’ role in monitoring endocrine disorders, cancer, and viral infections. Advances in technologies like microfluidics, biosensors, and next-generation sequencing have enhanced the sensitivity and specificity of salivary diagnostics, making it a viable alternative to blood-based diagnostics. The review also evaluates challenges such as the need for standardized collection protocols, variability in salivary composition, and the integration of these technologies into clinical workflows. The findings emphasize the transformative potential of saliva in personalized medicine, especially for early disease detection and real-time health monitoring. Practical applications include its use in mass screenings and public health crises, highlighting saliva as a cornerstone for future advancements in non-invasive diagnostics.

Teledentistry enhances access to oral healthcare by enabling remote consultations, diagnosis, and patient management. This paper explores its applications, benefits, challenges, and impact on modern dentistry. A comprehensive review of existing literature and case studies was conducted to examine the effectiveness of teledentistry. Key aspects analyzed include digital imaging, AI (artificial intelligence)-assisted diagnostics, and cloud-based patient records, which facilitate early disease detection, reduce wait times, and minimize unnecessary visits. The review also highlights how teledentistry improves collaboration among dental professionals for better treatment planning. Challenges include legal barriers, data security concerns, and limited digital infrastructure. Standardized protocols and professional training are essential for effective implementation. Future advancements in AI and telecommunication technologies will further integrate teledentistry into standard practice, improving accessibility and efficiency in oral healthcare.

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 revolutionizing the field of oral and dental healthcare by offering innovative tools and techniques for optimizing diagnosis, treatment planning, and patient management. This narrative review explores the current applications of AI in dentistry, focusing on its role in enhancing diagnostic accuracy and efficiency. AI technologies, such as machine learning, deep learning, and computer vision, are increasingly being integrated into dental practice to analyze clinical images, identify pathological conditions, and predict disease progression. By utilizing AI algorithms, dental professionals can detect issues like caries, periodontal disease and oral cancer at an earlier stage, thus improving patient outcomes.

Rheumatoid arthritis (RA) and periodontal disease (PD) are chronic complex inflammatory diseases with several common susceptibility factors, especially genetic and environmental risk factors. Although both disorders involve a perturbation of the immune–inflammatory response at multiple levels, one major difference between the two is the different locations in which they develop. RA is triggered by an exaggerated autoimmune response that targets joints, while periodontal disease occurs as a consequence of the subgingival periodontopathogenic microbiota. Current treatment models in both pathologies involve the stratification of patients to allow therapeutic individualization according to disease stage, complexity, progression, lifestyle, risk factors, and additional systemic diseases. Therapeutic guidelines for RA comprise of five main classes of drugs: non-steroidal anti-inflammatory drugs (NSAIDs), analgesics, glucocorticoids, and disease-modifying anti-rheumatic drugs (DMARDs): biologic and non-biologic. Although various treatment options are available, a definitive treatment remains elusive, therefore research is ongoing in this area. Several alternatives are currently being tested, such as matrix metalloproteinases (MMP) inhibitors, toll-like receptors (TLR) blockers, pro-resolution mediators, anti-hypoxia inducing factors, stem cell therapy, NLRP3 inhibitors and even natural derived compounds. Although the link between PD and rheumatoid arthritis has been investigated by multiple microbiology and immunology studies, the precise influence and causality is still debated in the literature. Furthermore, the immunomodulatory effect of anti-rheumatic drugs on the periodontium is still largely unknown. In this narrative review, we explore the mechanisms of interaction and the potential influence that anti-rheumatoid medication, including novel treatment options, has on periodontal tissues and whether periodontal health status and treatment can improve the prognosis of an RA patient.

Orthodontic treatment in patients with no periodontal tissue breakdown vs. horizontal bone loss should be approached with caution even though it can bring significant benefits in terms of periodontal recovery and long-term success. We used the finite element method (FEM) to simulate various clinical scenarios regarding the periodontal involvement: healthy with no horizontal bone loss, moderate periodontal damage (33%) and severe horizontal bone loss (66%). Afterwards, forces of different magnitudes (0.25 N, 1 N, 3 N, and 5 N) were applied in order to observe the behavioral patterns. Through mathematical modeling, we recorded the maximum equivalent stresses (σ ech), the stresses on the direction of force application (σ c) and the displacements produced (f) in the whole tooth–periodontal ligament–alveolar bone complex with various degrees of periodontal damage. The magnitude of lingualization forces in the lower anterior teeth influences primarily the values of equivalent tension, then those of the tensions in the direction in which the force is applied, and lastly those of the displacement of the lower central incisor. However, in the case of the lower lateral incisor, it influences primarily the values of the tensions in the direction in which the force is applied, then those of equivalent tensions, and lastly those of displacement. Anatomical particularities should also be considered since they may contribute to increased periodontal risk in case of lingualization of the LLI compared to that of the LCI, with a potential emergence of the “wedge effect”. To minimize periodontal hazards, the orthodontic force applied on anterior teeth with affected periodontium should not exceed 1 N.