<br />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.<br /><br />History of AI<br />Artificial intelligence is not a new term. Alan Turing wrote in his paper “Computing Machinery and Intelligence” (9) in the 1950 issue of Mind:<br /><br />“I believe that at the end of the century (20th), the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.”<br />Back then, there was no term to interpret AI; Turing described AI as “machines thinking”. He mathematically investigated the feasibility of AI and explored how to construct intelligent machines and assess machine intelligence. He proposed that humans solve problems and make decisions by utilizing available information and inference, machines also can do the same thing.<br /><br />AI in dentistry<br />As in other industries, AI in dentistry has started to blossom in recent years. From a dental perspective, applications of AI can be classified into diagnosis, decision-making, treatment planning, and prediction of treatment outcomes. Among all the AI applications in dentistry, the most popular one is diagnosis. AI can make more accurate and efficient diagnoses, thus reducing dentists' workload. On one hand, dentists are increasingly relying on computer programs for making decisions (36, 37). On the other hand, computer programs for dental use are becoming more and more intelligent, accurate, and reliable. Research on AI has spread over all fields in dentistry.<br /><br />Although a large amount of journal articles regarding dental AI have been published, it is still difficult to compare between articles in terms of study design, data allocation (i.e., training, test, and validation sets), and model performance (i.e., accuracy, sensitivity, specificity, F1, AUC {Area Under [the receiver operating characteristic (ROC)] Curve}, recall). Most articles failed to report the information mentioned above entirely. Thus, the MI-CLAIM (Minimum Information about Clinical Artificial Intelligence Modeling) checklist has been advocated to bring similar levels of transparency and utility to the application of AI in medicine.<br />AI in prosthodontics<br />In prosthodontics, a typical treatment process to prepare a dental crown includes tooth preparation, impression taking, cast trimming, restoration design, fabrication, try-in, and cementation. The application of AI in prosthodontics mainly lies in the restoration design (Table 6). CAD/CAM has digitalized the design work in commercialized products, including CEREC, Sirona, 3Shape, etc. Although this has dramatically increased the efficiency of the design process by utilizing a tooth library for crown design, it still cannot achieve a custom-made design for individual patients. With the development of AI, Hwang et al. and Tian et al. proposed novel approaches based on 2D-GAN models to generate a crown by learning from technicians' designs. The training data was 2D depth maps converted from 3D tooth models. Ding reported a 3D-DCGAN network in the crown generation, which utilized 3D data directly in the crown generation process, the morphology of generated crowns was similar compared with natural teeth. Integrating AI with CAD/CAM or 3D/4D printing can achieve a more desirable workflow with high efficiency. AI has also been used in shade matching and debonding prediction of CAD/CAM restoration.<br />Apart from fixed prosthodontics, the design in removable prosthodontics is more challenging as more factors and variables need to be considered. No ML algorithm is available for the purpose of designing removable dentures while several expert (knowledge based) systems have been introduced). Current ML algorithms are more focused on assisting the design process of removable dentures, e.g., classification of dental arches), and facial appearance prediction in edentulous patients.<br />