Temporomandibular Disorders (TMDs) are a group of conditions affecting the temporomandibular joint and related structures, frequently encountered in dental and medical practice. With the increasing use of large language models (LLMs) for health-related queries, this study aimed to evaluate the quality of responses provided by four leading AI platforms Google Gemini, Microsoft Copilot, ChatGPT, and Meta AI to the most commonly asked questions about TMDs. A set of 45 standardized questions covering definitions, symptoms, causes, diagnosis, and management of TMDs was used. Responses from each platform were assessed on a Likert scale (1–20) across four key domains: Understandability, Readability, Credibility, and Contextual Relevance. While all four AI models demonstrated potential as educational tools for patients seeking information about TMDs, their quality and consistency varied. Based on this Likert-scale assessment, the overall ranking was: Google Gemini (1st) > Microsoft Copilot (2nd) > ChatGPT (3rd) > Meta AI (4th).

Background: Orthodontic auxiliaries are frequently reused and may act as potential sources of cross-infection if not adequately sterilized. Limited comparative data exist regarding the effectiveness of commonly used sterilization methods for orthodontic materials. Aim: To evaluate and compare the effectiveness of different sterilization and disinfection methods in eliminating microbial contamination from selected orthodontic materials. Materials and Methods: This in-vitro microbiological study evaluated 48 orthodontic samples including NiTi closed coil springs, pre-formed molar bands with buccal tubes, and Class II elastics. Samples were divided into five groups: control, 70% ethanol, 2% glutaraldehyde, ultraviolet (UV) irradiation, and autoclaving. Following sterilization, all specimens were cultured on tryptic soy agar and incubated at 37°C for 72 hours. Microbial growth was assessed visually. Statistical analysis was performed using chi-square test. Results: All unsterilized samples demonstrated microbial growth. No microbial growth was observed in any samples treated with 70% ethanol, 2% glutaraldehyde, UV irradiation, or autoclaving. Statistically significant differences were observed between control and treated groups (Chi-square = 8.778, p = 0.003). Conclusion: All evaluated sterilization methods were effective in eliminating microbial contamination from orthodontic materials. Autoclaving and glutaraldehyde immersion are recommended as primary methods, while UV irradiation and ethanol can serve as adjunctive alternatives.

Malocclusion is a highly prevalent developmental condition in children and adolescents and represents one of the most frequent reasons for referral from primary dental care to orthodontic services [1, 2]. General Dental Practitioners (GDPs), particularly newly qualified dentists, often face uncertainty when assessing malocclusion severity, determining the optimal timing of referral, and establishing eligibility for National Health Service (NHS) orthodontic treatment [11, 12]. This uncertainty may result in delayed referral of high-risk cases or inappropriate referral of children with minimal treatment need, placing unnecessary pressure on specialist services [13]. This narrative review proposes a stepwise orthodontic referral ladder, translating the Index of Orthodontic Treatment Need (IOTN) into a clinically intuitive and structured decision-making framework. The model classifies malocclusion from mild to severe, incorporates red-flag conditions requiring early or urgent referral, integrates optimal age for referral, and aligns with UK NHS commissioning and British Orthodontic Society guidance [18–20]. The framework aims to provide GDPs and new graduates with a clear, defensible, and patient-centred reference scale to support consistent orthodontic referral decisions and improve outcomes for children.

Artificial intelligence (AI) has emerged as a transformative technology in modern orthodontics, redefining conventional diagnostic and therapeutic workflows through digital integration and predictive analytics. The incorporation of machine learning, deep learning, convolutional neural networks, and computer vision into orthodontic practice has significantly improved the accuracy of cephalometric landmark identification, malocclusion classification, treatment simulation, aligner therapy planning, and remote patient monitoring. Digital orthodontics, supported by intraoral scanners, cone-beam computed tomography (CBCT), three-dimensional imaging, and cloud-based systems, has created a robust data-driven ecosystem that facilitates AI-assisted clinical decision-making. AI-based software systems are increasingly capable of reducing operator variability, minimizing human error, and improving clinical efficiency while enabling personalized orthodontic care. Furthermore, teleorthodontics and AI-enabled remote monitoring systems have expanded patient accessibility and compliance tracking. Despite these advancements, important concerns remain regarding algorithm transparency, ethical considerations, data privacy, medico-legal accountability, and clinician dependency on automated systems. Current evidence suggests that AI should function as an adjunctive clinical tool rather than a replacement for professional judgment. The present review comprehensively discusses the evolution, applications, advantages, limitations, ethical implications, and future prospects of artificial intelligence in digital orthodontics. The article highlights the growing role of AI in precision orthodontics and emphasizes the need for standardized validation and responsible clinical integration.

Biomimetic restorative dentistry (BRD), a paradigm change from conventional dental techniques, aims to restore damaged teeth by mimicking their natural appearance, functionality, and structure. This multidisciplinary discipline uses biological processes as inspiration to develop cutting-edge dental treatments that blend in perfectly with the natural tissues of teeth. In contrast to conventional techniques, which frequently entail significant tooth reduction and the use of inflexible, incompatible materials, BRD places a higher priority on maintaining healthy tooth structure, which improves the endurance, durability, and aesthetics of restorations. This review examines the basic concepts, range of materials, state-of-the-art clinical techniques, and creative applications of biomimetics in dentistry.

To review current evidence on the epidemiology, pathophysiology, diagnosis, and management of sleep bruxism [SB] and its association with temporomandibular disorders [TMD]. A narrative review of recent literature was conducted, focusing on prevalence, diagnostic methods, clinical manifestations, and therapeutic strategies for SB and TMD. SB is increasingly recognized as a multifactorial condition with neurological, behavioral, and environmental determinants. Its frequent association with TMD complicates diagnosis and management. Advances in diagnostic technologies, including polysomnography, electromyography, and AI-assisted sleep analysis, have improved diagnostic precision. Management strategies include behavioral interventions, occlusal splints, pharmacologic options, and multidisciplinary care, with pediatric cases emphasizing conservative measures. SB and TMD are intricately linked conditions requiring a multidisciplinary diagnostic and therapeutic approach. Future research should focus on standardizing pediatric diagnostic criteria and assessing long-term outcomes of therapeutic interventions.

Alveolar ridge deficiency following tooth extraction, trauma, periodontal disease, and long-term edentulism presents a major challenge in implant rehabilitation. Adequate bone volume is essential for ideal implant positioning, long-term osseointegration, esthetic success, and functional stability. Autogenous bone grafting continues to be regarded as the gold standard in ridge augmentation because of its osteogenic, osteoinductive, and osteoconductive properties. However, contemporary regenerative dentistry has introduced multiple evidence-based approaches that improve the predictability of horizontal and vertical ridge reconstruction while reducing morbidity and graft resorption. This review discusses the biologic basis of alveolar ridge resorption and critically evaluates current decision-making principles in horizontal and vertical ridge augmentation. Various reconstructive modalities including guided bone regeneration, autogenous block grafting, shell techniques, titanium mesh-assisted augmentation, distraction osteogenesis, and biologically enhanced regenerative procedures are analyzed with emphasis on clinical indications, advantages, limitations, and evidence-based outcomes. Horizontal ridge augmentation procedures generally demonstrate greater predictability and lower complication rates compared with vertical reconstruction, which remains surgically demanding because of limited vascularity, soft tissue tension, and graft instability. Recent evidence supports the use of combination grafting protocols involving autogenous bone and slowly resorbing biomaterials to enhance dimensional stability and reduce postoperative resorption. Digital technologies including cone-beam computed tomography, CAD/CAM-guided reconstruction, and customized titanium meshes have further improved surgical precision and treatment outcomes. Successful alveolar ridge reconstruction depends on careful defect analysis, individualized treatment planning, biologic principles, and meticulous soft tissue management. Contemporary evidence indicates that autogenous ridge augmentation remains the most reliable option for complex alveolar reconstruction despite ongoing advances in biomaterials and tissue engineering.