Artificial intelligence (AI) has made deep inroads into dentistry in the last few years. The aim of this systematic review was to identify the development of AI applications that are widely employed in dentistry and evaluate their performance in terms of diagnosis, clinical decision-making, and predicting the prognosis of the treatment. The literature for this paper was identified and selected by performing a thorough search in the electronic data bases like PubMed, Medline, Embase, Cochrane, Google scholar, Scopus, Web of science, and Saudi digital library published over the past two decades (January 2000–March 15, 2020).After applying inclusion and exclusion criteria, 43 articles were read in full and critically analyzed. Quality analysis was performed using QUADAS-2. AI technologies are widely implemented in a wide range of dentistry specialties. Most of the documented work is focused on AI models that rely on convolutional neural networks (CNNs) and artificial neural networks (ANNs). These AI models have been used in detection and diagnosis of dental caries, vertical root fractures, apical lesions, salivary gland diseases, maxillary sinusitis, maxillofacial cysts, cervical lymph nodes metastasis, osteoporosis, cancerous lesions, alveolar bone loss, predicting orthodontic extractions, need for orthodontic treatments, cephalometric analysis, age and gender determination. These studies indicate that the performance of an AI based automated system is excellent. They mimic the precision and accuracy of trained specialists, in some studies it was found that these systems were even able to outmatch dental specialists in terms of performance and accuracy.

Oral cancer (OC) is a deadly disease with a high mortality and complex etiology. Artificial intelligence (AI) is one of the outstanding innovations in technology used in dental science. This paper intends to report on the application and performance of AI in diagnosis and predicting the occurrence of OC. In this study, we carried out data search through an electronic search in several renowned databases, which mainly included PubMed, Google Scholar, Scopus, Embase, Cochrane, Web of Science, and the Saudi Digital Library for articles that were published between January 2000 to March 2021. We included 16 articles that met the eligibility criteria and were critically analyzed using QUADAS-2. AI can precisely analyze an enormous dataset of images (fluorescent, hyperspectral, cytology, CT images, etc.) to diagnose OC. AI can accurately predict the occurrence of OC, as compared to conventional methods, by analyzing predisposing factors like age, gender, tobacco habits, and bio-markers. The precision and accuracy of AI in diagnosis as well as predicting the occurrence are higher than the current, existing clinical strategies, as well as conventional statistics like cox regression analysis and logistic regression.

Forensic odontology (FO) is regarded in the literature as one of the most reliable and economical scientific methods for victim identification in mass disasters (MDs). The present paper systematically reviews the role of forensic odontologists in various global MDs. A comprehensive search of the literature databases (PubMed, Medline, SCOPUS, Web of Science and Google Scholar), along with cross-referencing published peer-reviewed articles, was conducted. The search included full texts, abstracts or titles, had no inclusion year limit (searched until September 2017) and was limited to the English language. Keywords included a combination of ‘Forensic odontology’, ‘Dental records’, ‘Victim identification’, ‘Natural mass disaster’, ‘Criminal mass disaster’, ‘Accidental mass disaster’ and ‘Victim disaster’. Of the included disasters (20), 12 (57.14%) were accidental, 5 (23.80%) natural and 3 (19.04%) were criminal. The maximum number of victims was associated with the Japan tsunami (15892), followed by the Thailand tsunami (4280) and the Estonia ferry disaster (852). A total of 23654 victims were reported, of which 20569 (86.96%) were positively identified. Reports from 17 MDs included the use of FO in victim identification [3025 (14.70%) cases]. In addition, 1094 victims (5.31%; from 7 papers) were identified using FO in combination with other methodologies. The highest percentage of victims was identified using FO following the Kentucky air crash (47; 100%), followed by the Newark air crash (38; 76%), the Nepal air crash (10; 71.42%), the France air crash (56; 65.88%), the Australian bushfire (14; 63.63%), and the Estonia ferry disaster (57; 60.63%). FO has played a significant role in victim identification in several MDs around the world. The success of FO-based identification is heavily dependent on the availability of ante-mortem records from general dental practitioners. Hence, adequate knowledge about FO and appropriate dental record keeping among general dental practitioners are critical.