## 3 Literature Review  
Artificial intelligence (AI) incorporation into healthcare systems has created new opportunities to enhance clinical results and operational effectiveness. Large volumes of data may be processed quickly and accurately by AI-powered systems, yielding insights that would be challenging to obtain manually. To help with early diagnosis, AI algorithms, for instance, can examine medical photos to find patterns of particular diseases [12]. Furthermore, according to [13], AI-powered predictive analytics can lessen stress in healthcare facilities, help with resource allocation, and predict patient admissions. Many researchers have studied the influencing factors of medical information technology adoption based on theories like technology adoption behavior to identify the critical elements that influence adopters' adoption of medical information technology and its related applications and to encourage the use and promotion of medical information technology [14; 15; 16]. The proposed work in [17] describes the creation and evaluation of YOLOv3, a deep learning model for the automated identification and categorization of four prevalent dental conditions in orthopantomography (OPG) X-ray pictures. Because there were no publicly accessible datasets, the researchers augmented the original 800 OPG photos to build their collection of 1200 images. The study addresses the drawbacks of manual analysis, which can be laborious and prone to human mistakes. It emphasizes the potential of deep learning to increase the effectiveness and accuracy of dental diagnosis. The research presented in [18] describes the creation and verification of an affordable artificial intelligence system that uses non-standard smartphone photos to identify dental cavities. The researchers used a hybrid model that combined transfer learning with other deep learning models (e.g., VGG16) and Ensemble YOLO object identification methods. Variations in image quality were addressed through the use of data augmentation techniques. By concentrating on non-standardized photos taken using widely accessible technologies, the study overcomes the shortcomings of current AI-based caries detection techniques. There is also a discussion on future research directions. The authors in [19] analyzed 22 studies that employ machine learning to diagnose oral health problems using X-rays from 2018 to 2022. The study draws attention to both achievements and shortcomings, including sparse datasets and uneven reporting. For more dependable clinical applications, the authors emphasize the necessity of larger, diversified datasets and standardized procedures. [PERSON], [PERSON], et al. [20] employed deep learning to identify teeth in dental periapical radiographs. R-FCN (Region-based Fully Convolutional Networks) was combined with a faster R-CNN to detect typical dental issues such as tooth loss, decay, and fillings. [PERSON] et al. [21] used a deep convolutional neural network (CNN) to evaluate radiographs and determine each tooth's radiographic bone loss (RBL). The updated CNN periodontitis categorization's RBL, staging, and presumptive diagnosis were compared to independent examiner findings. However, these algorithms are just concerned with detecting teeth, not identifying each of their faces. [PERSON] et al. applied CNNs to detect vertical root fractures (VRF) in panoramic radiography [22]. The CNN was trained using Detect Net five-fold cross-validation, and DIGITS version 5.0 was utilized to improve model reliability. In Ref. [23], this paper describes a novel AI-driven approach for automatically segmenting the PDL (Periodontal Ligament) on CBCT (Cone Beam Computed Tomography) imaging, setting the groundwork for future deep-learning models that can calculate the PDL area or volume directly from CBCT 3D pictures. The study team aims to improve and refine the PDL segmentation system so that periodontitis, orthodontists, prosthodontists, and implantologists may diagnose and plan treatments more efficiently and accurately. [PERSON] et al.