SYSTEMATIC REVIEW Artificial intelligence models for tooth-supported fixed and removable prosthodontics: A systematic review Marta Revilla-León, DDS, MSD, a Miguel Gómez-Polo, DDS, PhD, b Shantanu Vyas, c Basir A. Barmak, MD, MSc, EdD, d German O. Gallucci, DMD, PhD, e Wael Att, DDS, Dr Med Dent, PhD, f Mutlu Özcan, DDS, DMD, PhD, g and Vinayak R. Krishnamurthy, PhD h Artificial intelligence (AI) models have been applied in different dental specialties. 1-4 In restorative dentistry, AI has been applied to improve dental caries diagnosis by using periapical and bitewing radiographs, 5-7 predict the failure of a dental restoration, 8,9 and diagnose vertical tooth fracture by using periapical radiographs or cone beam computed tomography data. 10,11 In implant dentistry, AI has been used to identify implant type from This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. a Assistant Professor and Assistant Program Director AEGD Residency, College of Dentistry, Texas A&M University, Dallas, Texas; and Affiliate Faculty Graduate Prosthodontics, School of Dentistry, University of Washington, Seattle, Wash; and Researcher at Revilla Research Center, Madrid, Spain. b Associate Professor, Department of Conservative Dentistry and Prosthodontics, School of Dentistry, Complutense University of Madrid, Madrid, Spain. c Graduate Research Assistant, J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, Texas. d Assistant Professor Clinical Research and Biostatistics, Eastman Institute of Oral Health, University of Rochester Medical Center, Rochester, NY. e Raymond J. and Elva Pomfret Nagle Associate Professor of Restorative Dentistry and Biomaterials Sciences and Chair of the Department of Restorative Dentistry and Biomaterials Science, Harvard School of Dental Medicine, Boston, Mass. f Professor and Chair Department of Prosthodontics, Tufts University School of Dental Medicine, Boston, Mass. g Professor and Head, Division of Dental Biomaterials, Center of Dental Medicine, Clinic of Reconstructive Dentistry, University of Zürich, Zürich, Switzerland. h Assistant Professor, J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, Texas. ABSTRACT Statement of problem. Artificial intelligence applications are increasing in prosthodontics. Still, the current development and performance of artificial intelligence in prosthodontic applications has not yet been systematically documented and analyzed. Purpose. The purpose of this systematic review was to assess the performance of the artificial intelligence models in prosthodontics for tooth shade selection, automation of restoration design, mapping the tooth preparation finishing line, optimizing the manufacturing casting, predicting facial changes in patients with removable prostheses, and designing removable partial dentures. Material and methods. An electronic systematic review was performed in MEDLINE/PubMed, EMBASE, Web of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with artificial intelligence models were selected based on 6 criteria: tooth shade selection, automated fabrication of dental restorations, mapping the finishing line of tooth preparations, optimizing the manufacturing casting process, predicting facial changes in patients with removable prostheses, and designing removable partial dentures. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute Critical Appraisal Checklist for Quasi- Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. Results. A total of 36 articles were reviewed and classified into 6 groups based on the application of the artificial intelligence model. One article reported on the development of an artificial intelligence model for tooth shade selection, reporting better shade matching than with conventional visual selection; 14 articles reported on the feasibility of automated design of dental restorations using different artificial intelligence models; 1 artificial intelligence model was able to mark the margin line without manual interaction with an average accuracy ranging from 90.6% to 97.4%; 2 investigations developed artificial intelligence algorithms for optimizing the manufacturing casting process, reporting an improvement of the design process, minimizing the porosity on the cast metal, and reducing the overall manufacturing time; 1 study proposed an artificial intelligence model that was able to predict facial changes in patients using removable prostheses; and 17 investigations that developed clinical decision support, expert systems for designing removable partial dentures for clinicians and educational purposes, computer-aided learning with video interactive programs for student learning, and automated removable partial denture design. Conclusions. Artificial intelligence models have shown the potential for providing a reliable diagnostic tool for tooth shade selection, automated restoration design, mapping the preparation finishing line, optimizing the manufacturing casting, predicting facial changes in patients with removable prostheses, and designing removable partial dentures, but they are still in development. Additional studies are needed to further develop and assess their clinical performance. (J Prosthet Dent 2021;-:---) THE JOURNAL OF PROSTHETIC DENTISTRY 1