Artificial Intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence. AI is not a new term, the concept of AI can be dated back to 1950. However, it did not become a practical tool until two decades ago. Owing to the rapid development of three cornerstones of current AI technology—big data (coming through digital devices), computational power, and AI algorithm—in the past two decades, AI applications have started to provide convenience to people's lives. In dentistry, AI has been adopted in all dental disciplines, i.e., operative dentistry, periodontics, orthodontics, oral and maxillofacial surgery, and prosthodontics. The majority of the AI applications in dentistry are for diagnosis based on radiographic or optical images, while other tasks are not as applicable as image-based tasks mainly due to the constraints of data availability, data uniformity, and computational power for handling 3D data. Evidence-based dentistry (EBD) is regarded as the gold standard for decision making by dental professionals, while AI machine learning (ML) models learn from human expertise. ML can be seen as another valuable tool to assist dental professionals in multiple stages of clinical cases. This review describes the history and classification of AI, summarizes AI applications in dentistry, discusses the relationship between EBD and ML, and aims to help dental professionals better understand AI as a tool to support their routine work with improved efficiency.

Titanium (Ti) has been used for long in dentistry and medicine for implant purpose. During the years, not only the commercially pure Ti but also some alloys such as binary and tertiary Ti alloys were used. The aim of this review is to describe and compare the current literature on binary Ti alloys, including Ti–Zr, Ti–In, Ti–Ag, Ti–Cu, Ti–Au, Ti–Pd, Ti–Nb, Ti–Mn, Ti–Mo, Ti–Cr, Ti–Co, Ti–Sn, Ti–Ge and Ti–Ga, in particular to mechanical, chemical and biological parameters related to implant application. Literature was searched using the PubMed and Web of Science databases, as well as google without limiting the year, but with principle key terms such as ‘ Ti alloy’, ‘binary Ti ’, ‘Ti-X’ (with X is the alloy element), ‘dental implant’ and ‘medical implant’. Only laboratory studies that intentionally for implant or biomedical applications were included. According to available literatures, we might conclude that most of the binary Ti alloys with alloying <20% elements of Zr, In, Ag, Cu, Au, Pd, Nb, Mn, Cr, Mo, Sn and Co have high potential as implant materials, due to good mechanical performance without compromising the biocompatibility and biological behaviour compare to cp-Ti.

A compact and dexterous magnetic resonance (MR)–safe soft robot enables MR imaging–guided transoral laser microsurgery.

To develop a risk of bias tool for pre-clinical dental materials research studies that aims to support reporting of future investigations and improve assessment in systematic reviews. A four-stage process following EQUATOR network recommendations was followed, which included project launch, literature review, Delphi process and the tool finalization. With the support of the European Federation of Conservative Dentistry (EFCD) and the Dental Materials Group of the International Association for Dental Research (DMG-IADR), a total of 26 expert stakeholders were included in the development and Delphi vote of the initial proposal. The proposal was built using data gathered from the literature review stage. During this stage, recent systematic reviews featuring dental materials research, and risk of bias tools found in the literature were comprehensively scanned for bias sources. The experts thus reached a consensus for the items, domains and judgement related to the tool, allowing a detailed guide for each item and corresponding signalling questions. The tool features nine items in total, spread between 4 domains, pertaining to the following types of bias: bias related to planning and allocation (D1), specimen preparation (D2), outcome assessment (D3) and data treatment and outcome reporting (D4). RoBDEMAT, as presented, features signalling questions and a guide that can be used for RoB judgement. Its use as a checklist is preferred over a final summary score. RoBDEMAT is the first risk of bias tool for pre-clinical dental materials research, supported and developed by a broad group of expert stakeholders in the field, validating its future use. This new tool will contribute the study field by improving the scientific quality and rigour of dental materials research studies and their systematic reviews. Such studies are the foundation and support of future clinical research and evidence-based decisions.