Three-dimensional printing has transformed dentistry by enabling the production of customized dental restorations, aligners, surgical guides, and implants. A variety of polymers and composites are used, each with distinct properties. This review explores materials used in 3D printing for dental applications, focusing on trends identified through a literature search in PubMed, Scopus, and the Web of Science. The most studied areas include 3D-printed crowns, bridges, removable prostheses, surgical guides, and aligners. The development of new materials is still ongoing and also holds great promise in terms of environmentally friendly technologies. Modern manufacturing technologies have a promising future in all areas of dentistry: prosthetics, periodontology, dental and oral surgery, implantology, orthodontics, and regenerative dentistry. However, further studies are needed to safely introduce the latest materials, such as nanodiamond-reinforced PMMA, PLA reinforced with nanohydroxyapatite or magnesium, PLGA composites with tricalcium phosphate and magnesium, and PEEK reinforced with hydroxyapatite or titanium into clinical practice.

Artificial Intelligence (AI) has introduced transformative possibilities in orthodontics by enhancing diagnostic precision, treatment planning, and aesthetic outcomes. In face-driven orthodontics, treatment objectives extend beyond achieving proper occlusion to optimizing facial balance and harmony. With the growing patient demand for aesthetic improvements, AI technologies enable clinicians to integrate facial analysis and dynamic soft-tissue evaluation into personalized treatment approaches. Research in this scoping review analyzed current applications of AI in face-driven orthodontics, focusing on diagnosis, soft-tissue assessment, and individualized treatment planning. A comprehensive search was conducted in PubMed and Scopus for studies published between 2021 and 2025. The review followed the PRISMA-ScR guidelines. Of 54 initially identified studies, 24 met the inclusion criteria after title, abstract, and full-text screening. Extracted data were organized according to the main application areas of AI in face-driven orthodontics. Most studies focused on AI-assisted facial analysis, 3D reconstruction, and treatment simulation. Deep learning models demonstrated high performance in soft-tissue prediction, aesthetic evaluation, and diagnostic accuracy. However, heterogeneity in datasets, a lack of standardized validation protocols, limited external validation across included studies and limited clinical applicability were identified as key limitations. AI-based facial analysis supports a shift toward individualized, aesthetics-oriented orthodontic planning. Although current evidence highlights its potential for improving diagnostic precision and treatment outcomes, further validation through large-scale clinical studies is essential for broader implementation in everyday practice.

Background/Objectives: Artificial intelligence (AI) has gained growing interest in the field of orthognathic surgery due to its potential to improve diagnostic accuracy, surgical planning, and treatment outcomes. This scoping review maps literature from 2017 to May 2025 to identify AI applications in orthognathic surgery, assess their clinical relevance, and discuss the associated ethical, legal, and technical limitations. Methods: This scoping review further examines the stages of the orthognathic surgical workflow at which AI applications have been prospectively validated, the artificial intelligence methodologies applied to virtual surgical planning and outcome prediction, and the main methodological, ethical, and legal factors that may constrain broader clinical adoption. Results: A total of 62 studies were included, covering AI use in cephalometric analysis, virtual surgical planning (VSP), outcome prediction, and intraoperative support. While AI demonstrates remarkable potential in orthognathic planning, current approaches are often limited by heterogeneous methodologies and retrospective validation. Conclusions: Future studies should prioritize prospective, multicentre designs integrating AI-assisted decision-making directly into the clinical workflow, with emphasis on model interpretability, patient-specific accuracy, and ethical transparency. These questions extend beyond mapping applications by emphasizing clinical validation, methodological rigor, and ethical accountability—dimensions insufficiently explored in prior reviews.

Recent advances in artificial intelligence (AI), robotics, and additive manufacturing are expanding the capabilities of digital orthodontic workflows. This scoping review examines the evolving role of AI and robotics in orthodontics, with particular emphasis on direct 3D and 4D printing of orthodontic appliances from shape memory materials. A systematic search of the Scopus database identified 278 records published between 2020 and 2025, of which 24 studies were included in the review following PRISMA-guided screening. The reviewed literature indicates growing interest in AI-assisted treatment planning, digital workflow optimization, and additive manufacturing approaches that may support personalized fabrication of orthodontic appliances. Shape memory polymers and 4D-printing concepts show potential for controlled, time-dependent force delivery and a reduced need for sequential appliance replacement. However, the current evidence remains predominantly experimental, and broader clinical implementation is limited by insufficient clinical validation, challenges related to material standardization, and unresolved biocompatibility concerns.

Artificial intelligence (AI) and additive manufacturing are increasingly integrated into digital orthodontic workflows, but orthodontic appliance design remains less automated than manufacturing. This narrative review synthesizes the current role of AI-related methods in automated and semi-automated orthodontic appliance geometry design within three-dimensional printing workflows. PubMed, Scopus, and Web of Science were searched for publications from 2015 to 2026, with selected foundational sources included. Current AI-related methods support segmentation, anatomical recognition, surface alignment, treatment-planning support, monitoring, and computer-aided design refinement rather than autonomous geometry generation. Higher automation is most evident in standardized appliances, particularly occlusal splints, clear aligners, and selected indirect bonding tray workflows, whereas fixed functional appliances, anchorage devices, TAD guides, and individualized wire-based constructions remain largely clinician-driven. Key barriers include anatomical variability, limited biomechanical-response prediction, limited explainability, regulatory uncertainty, and lack of appliance-specific datasets. Future progress will likely depend on clinician-supervised hybrid workflows integrating computational design support with biomechanical validation.