Precision in diagnosis is essential for achieving optimal outcomes in prosthodontics, orthodontics, and orthognathic treatments. Virtual articulators provide a sophisticated digital alternative to conventional methods, integrating intraoral scans, facial scans, and cone beam computed tomography (CBCT) to enhance treatment predictability. This review examines advancements in virtual articulator technology, including digital workflows, virtual facebow transfer, and occlusal analysis, with a focus on Artificial Intelligence (AI)-driven methodologies such as machine learning and artificial neural networks. The clinical implications, particularly in condylar guidance and sagittal condylar inclination, are investigated. By streamlining the acquisition and articulation of digital dental models, virtual articulators minimize material handling errors and optimize workflow efficiency. Advanced imaging techniques enable precise alignment of digital maxillary models within computer-aided design and computer-aided manufacturing systems (CAD/CAM), facilitating accurate occlusal simulations. However, challenges include potential distortions during digital file integration and the necessity for robust algorithms to enhance data superimposition accuracy. The adoption of virtual articulators represents a transformative advancement in digital dentistry, with promising implications for diagnostic precision and treatment outcomes. Nevertheless, further clinical validation is essential to ensure the reliable transfer of maxillary casts and refine digital algorithms. Future developments should prioritize the integration of AI to enhance predictive modeling, positioning virtual articulators as a standard tool in routine dental practice, thereby revolutionizing treatment planning and interdisciplinary collaboration. This review explores advancements in virtual articulators, focusing on their role in enhancing diagnostic precision, occlusal analysis, and treatment predictability. It examines digital workflows, AI-driven methodologies, and clinical applications while addressing challenges in data integration and algorithm optimization.

Tooth wear is a growing oral health concern with implications for function, esthetics, and psychological well-being, ultimately affecting oral health-related quality of life (OHRQoL). While clinical indices assess tooth wear severity, they fail to capture patient-reported outcomes (PROs) and patient-reported outcome measures (PROMs). This narrative review aims to identify and synthesize the use of PROs and PROMs used in adults with tooth wear and to map their assessed domains against the Wilson and Cleary model of health outcomes, highlighting gaps and guiding the development of condition-specific instruments. A comprehensive search of the literature was conducted across PubMed, MEDLINE, and Embase. Studies involving PROMs in adults with tooth wear were included. Extracted data encompassed psychometric properties and domains assessed. PROMs such as the OHIP, OES, OIDP, and QMFQ have been frequently used, focusing on functional limitation, esthetic perception, and psychological distress. However, no single instrument comprehensively addresses all relevant domains of the Wilson and Cleary model. Moreover, variation in tools and constructs limits comparability across studies and clinical settings. Existing PROMs capture only partial aspects of the patient experience related to tooth wear. When mapping these instruments to a validated theoretical model, significant gaps become evident, especially in terms of general health perceptions and overall quality of life metrics. To improve the evaluation and management of tooth wear in clinical settings, it is essential to create a condition-specific PROM based on a solid conceptual framework.

Precision in diagnosis is essential for achieving optimal outcomes in prosthodontics, or-thodontics, and orthognathic treatments. Virtual articulators provide a sophisticated digital alternative to conventional methods, integrating intraoral scans, facial scans, and CBCT to enhance treatment predictability. This review examines advancements in virtual articulator technology, including digital workflows, virtual facebow transfer, and occlusal analysis, with a focus on AI-driven methodologies such as machine learning and artificial neural networks. The clinical implications, particularly in condylar guidance and sagittal condylar inclination, are investigated. By streamlining the acquisition and articulation of digital dental models, virtual articulators minimize material handling errors and opti-mize workflow efficiency. Advanced imaging techniques enable precise alignment of digital maxillary models within CAD/CAM systems, facilitating accurate occlusal sim-ulations. However, challenges include potential distortions during digital file integration and the necessity for robust algorithms to enhance data superimposition accuracy. The adoption of virtual articulators represents a transformative advancement in digital den-tistry, with promising implications for diagnostic precision and treatment outcomes. Nevertheless, further clinical validation is essential to ensure the reliable transfer of maxillary casts and refine digital algorithms. Future developments should prioritize the integration of AI to enhance predictive modeling, positioning virtual articulators as a standard tool in routine dental practice, thereby revolutionizing treatment planning and interdisciplinary collaboration.

Background/Objectives: To characterize clinical values obtained from a digital jaw tracking system during the functional assessment of mandibular movements in patients undergoing prosthetic rehabilitation. Methods: Ten patients undergoing prosthetic rehabilitation were included. Mandibular movements were recorded using the Zebris JMA Jaw Tracking System and measurements including maximum mouth opening, lateral excursions, protrusion, and condylar path length. Descriptive analyses were performed. Quantitative parameters were summarized as mean ± standard deviation and range and compared with normative values reported in the literature. Results: Ten participants completed mandibular kinematics registration (8 female, 2 male). Considerable interindividual variability was observed across all parameters. Mean maximum opening was 38.19 ± 10.99 mm (range 22.5–59.6 mm), mean protrusion 5.79 ± 4.47 mm (range 3.0–17.8 mm), mean right lateral excursion 6.56 ± 3.06 mm, and mean left lateral excursion 4.90 ± 2.11 mm. Lateral asymmetry was identified in 5 out of 10 patients. Bilateral condylar path length asymmetry had a mean of 3.59 ± 1.89 mm. Conclusions: Values obtained through the jaw tracking system demonstrated considerable interindividual variability in this prosthetic rehabilitation population. These preliminary findings support the need for further research to characterize normative kinematic values in this specific population and to evaluate the clinical impact of integrating jaw tracking data into prosthetic planning workflows.