###### Abstract  
Artificial intelligence (AI) has garnered significant attention in recent years for its potential to revolutionize healthcare, including dentistry. However, despite the growing body of literature on AI-based dental image analysis, challenges such as the integration of AI into clinical workflows, variability in dataset quality, and the lack of standardized evaluation metrics remain largely unexplored. This systematic review aims to address these gaps by assessing the extent to which AI technologies have been integrated into dental specialties, with a specific focus on their applications in dental imaging. A comprehensive review of the literature was conducted, selecting relevant studies through electronic searches from Scopus, Google Scholar, and PubMed databases, covering publications from 2018 to 2023. A total of 52 articles were systematically analyzed to evaluate the diverse approaches of machine learning (ML) and deep learning (DL) in dental imaging. This review reveals that AI has become increasingly prevalent, with researchers predominantly employing convolutional neural networks (CNNs) for detection and diagnosis tasks. Pretrained networks demonstrate strong performance in many scenarios, while ML techniques have shown growing utility in estimation and classification. Key challenges identified include the need for larger, annotated datasets and the translation of research outcomes into clinical practice. The findings underscore AI's potential to significantly advance diagnostic support, particularly for non-specialist dentists, improving patient care and clinical efficiency. AI-driven software can enhance diagnostic accuracy, facilitate data sharing, and support collaboration among dental professionals. Future developments are anticipated to enable patient-specific optimization of restoration designs and implant placements, leveraging personalized data such as dental history, tissue type, and bone thickness to achieve better outcomes. artificial intelligence; dental images; CNN; machine learning; ANN; dentistry; image processing +
Footnote †: journal: _Algorithms_  
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AI has demonstrated in recent years how much it can help dentists meet patient demands, guarantee accurate diagnosis and treatment, and enhance oral healthcare. It helps to raise the standard of dental care by anticipating clinical case failures and offering workable remedies. Nowadays, the application of AI in dentistry, especially deep neural networks (DNNs), is progressively being used to support the analysis of medical images as a primary step in managing oral health and detecting oral diseases. In recent years, a growing body of research has indicated remarkable progress in the field of medical image interpretation using AI. Nonetheless, the transition from a manual feature extraction process to a more intricate and reliable end-to-end process happened gradually. Compared with many prior methods, the convolutional neural network (CNN) architecture showed remarkable performance as soon as it was utilized for end-to-end learning [8; 9; 10; 11]. This study makes the following significant contributions:  
* This review describes the current applications of AI in dentistry and demonstrates the recent AI technologies used in dental imaging. * We cover in detail how to build an AI-based dental image application such as the training dataset, image labeling, data augmentation, model training, and model evaluation. * We review the state-of-the-art articles from SCOPUS, Google Scholar, and PubMed during the last three years covering the application of AI in dental image analysis and interpretation. * We provide a brief introduction of several commonly used applications of AI in dentistry. * We review how AI techniques such as DL and ML have been used in areas such as tooth detection and labeling, dental caries, age and gender estimate, etc.