Citation: AbuSalim, S.; Zakaria, N.; Islam, M.R.; Kumar, G.; Mokhtar, N.; Abdulkadir, S.J. Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review. Healthcare 2022, 10, 1892. https://doi.org/10.3390/ healthcare10101892 Academic Editor: Takahiro Kanno Received: 31 July 2022 Accepted: 31 August 2022 Published: 28 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). healthcare Systematic Review Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review Samah AbuSalim 1 , Nordin Zakaria 1, *, Md Rafiqul Islam 2 , Ganesh Kumar 1 , Norehan Mokhtar 3 and Said Jadid Abdulkadir 1 1 Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia 2 Data Science Institute (DSI), University of Technology Sydney (UTS), Ultimo, Sydney 2007, Australia 3 Dental Simulation and Virtual Learning Research Excellence Consortium, Department of Dental Science, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas 13200, Penang, Malaysia * Correspondence: nordinzakaria@utp.edu.my Abstract: Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field. Keywords: dental informatics; dental practice; health informatics; dental diagnosis; deep learning 1. Introduction The use of information technology (IT) in healthcare practice and research is a global goal for many nations [1]. In the last fifty years, IT capabilities have advanced dramat- ically. Several advancements have enabled new and beneficial applications of IT in the medical field. The interdisciplinary discipline of medical informatics (MI) combines soft- ware, computer science, medicine, information science, statistics, cognitive sciences, and mathematics [2]. This field’s task and mission is to reduce costs while improving health care services, and also care errors by using concepts, tools, methods, software techniques, and modeling [3,4]. MI can be considered of as a subdiscipline of dental informatics (DI); hence MI has some influence on DI’s progress. Despite the similarities between DI and MI in medical research, it is important to perform separate studies that are specifically focused on DI. Information science and computer applications improve dental research, practice, management, and education, which has enormous potential in the relatively new field of DI. The use of computing in dentistry is only one aspect of DI. The initial practitioners of DI defined their strategy as the use of information science to address medical issues. More recent publications have described MI as a cascade from analysis to effect. A four-part structure is suggested by one previous study. The four parts are: formulation of the system development, evaluation, medical model, and system installation and modification. The inherent challenges at each phase in this procedure are the biggest challenge for much of DI [5]. Sadly, most dentists are unaware of what DI is, what its objectives are, what it has Healthcare 2022, 10, 1892. https://doi.org/10.3390/healthcare10101892 https://www.mdpi.com/journal/healthcare