## 4 Discussion  
The goal of this literature review was to visualize the state of the art of artificial intelligence in detecting different dental situations, such as the detection of teeth, caries, filled teeth, endodontic treatments and dental implants. Neural networks can have single or multiple layers, with nodes or neurons interconnected that allows signals to travel through the network. ANNs are typically divided into three layers of neurons, namely: input (receives the information), hidden (extracts patterns and performs the internal processing), and output (presents the final network output) [32; 33]. Training is the process to optimize parameters [34]. Figure 2 details the architecture for teeth detection. More and more industries are using artificial intelligence to make increasingly complex decisions, and many alternatives are available to them [32]. However, in view of our results, there is a paucity of guidance on selecting the appropriate methods tailored to the health-care industry. The benefit of neural networks in medicine and dentistry is related to their ability to process large amounts of data for analyzing, diagnosing and disease monitoring. Deep learning has become a great ally in the field of medicine in general and is beginning to be one in dentistry. According to the year of publication of the studies included in this review, 2019 was the year in which the most articles were published. The results provided by artificial intelligence have a great dependence on the data with which they learn and are trained, that is, on the input data and the image employed to detect each variable. All of the studies included in this review employed radiographs, mainly panoramic radiographies. In this sense, it would be interesting to apply neural networks and artificial intelligence in other types of radiological studies such as cone beam computed tomography (CBCT) or cephalometry, which allow clinicians to make a complete anatomical examination. [PERSON] et al. evaluated the detection and diagnosis of different lesions employing CBCT and a deep convolutional neural network [35]. Before being possible to detect the variables analyzed in this review, teeth must be detected. Panoramic radiography is the most common technique in general dentistry, which captures the entire mouth in a single 2D image [36; 37], and it is common to use artificial intelligence to detect the presence or absence of a tooth. The main advantages of these types of images are: the patient comfort compared with other techniques, such as intraoral images (bitewing and periapical); the low radiation exposure; and the ability to evaluate a larger area of the maxilla and mandible [37]. Panoramic radiographies are useful to evaluate endodontic treatments, periapical lesions and disorders in bones, among others [38]. This type of image has obtained the best results in tooth detection if we compare it with the study that used periapical images to detect this variable. In addition, the results obtained by the studies that detected teeth were superior to the rest of the variables analyzed, regardless of the network or type of image used. Caries is one of the most common chronic diseases in the oral field, with a great impact on a patient's health [39]. Clinical examination is the main method for caries detection, with radiographic examination being a complementary diagnostic tool [40]. According to experience and scientific literature, intraoral bitewing images are the most effective in detecting caries lesions [41]. However, only one study included in this review employed bitewings to detect caries. Two studies used near-infrared transillumination images and one employed periapical images. The best results were obtained in the study where periapical images were used to detect caries. A variety of CNN architectures were found in the studies included in this literature review. Convolutional networks are designed to process data that come in the form of multiple arrays and that are structured in a series of stages [42].