## 1 Introduction  
Digitalization and industry development are examples of new innovative technologies and processes in Industry 4.0. Digital technology is increasingly integrated into our daily lives. So, people's lives are more aided by electronic gadgets; the data those devices collect allows artificial intelligence (AI) to more readily use and analyze the data those devices produce. AI is rapidly growing and blooming throughout various sectors. Additionally, AI has been used in the fields of healthcare and dentistry, including drug development, wearable technology, hospital monitoring, robotic and automated decision support, precision and digital medicine, and medical and dental imaging diagnostics [1]. AI can be seen as a useful tool in many situations to assist healthcare professionals and dentists in lessening their workload [2]. Artificial neural networks (ANNs) can be used as clinical decision support systems (CDSS) to give medical professionals expert support [3]. Deep learning studies that efficiently diagnose dental disease and plan treatments are among the many ways artificial intelligence (AI) is being used in the fields of periodonontology, endodontics, orthodontics, restorative dentistry, and oral pathology. Nevertheless, due to restrictions on dataset availability, size, performance, and generalizability, there is very little proof of AI at this time. Throughout the past ten years, artificial intelligence has significantly benefited several dental subfields [4]. Therefore, there is a need to develop automated and accurate tools for detecting and classifying dental diseases using digital images. One such tool is the panoramic dental X-ray, or panorex. This two-dimensional (2-D) dental X-ray examination captures the entire mouth in a single image, including the teeth, upper and lower jaws, and surrounding structures and tissues [5]. In this paper, an integrated deep learning and Ensemble learning model for reliable and efficient dental healthcare is presented. The proposed model uses two state-of-the-art deep learning models, YOLO8 and RT-DETR, to detect dental diseases on panoramic X-ray images. YOLO8 is an improved version of the You Only Look Once (YOLO) family of models, which are fast and efficient for object detection tasks. The model works by analyzing the input image and predicting bounding boxes and class probabilities directly from the image in one evaluation, hence the name \"You Only Look Once\". Real-Time Detection Transformer (RT-DETR) is a cutting-edge end-to-end object detector developed by Baidu. It provides real-time performance while maintaining high accuracy. By separating intra-scale interaction and cross-scale fusion, RT-DETR effectively processes multiscale features by utilizing the power of Vision Transformers (ViT). It is highly adaptable, supporting the flexible adjustment of inference speed using different decoder layers without retraining. However, like any deep learning model, the performance of YOLO8 and RT-DETR heavily depends on the quality and diversity of the training data. We can summarize the major contributions of that paper as follows:  
1. We propose a new integrated artificial intelligence model for reliable and efficient dental healthcare that optimizes and guarantees the effectiveness of the detection of dental diseases, leading to better patient outcomes. 2. The proposed model helps a professional clinic achieve accurate automated diagnosis in minimal time and involves integrating various advanced technologies and methodologies. 3. The proposed model redefines the doctor-patient relationship, streamlining communication for easier, more consistent, and highly accurate interactions. 4. The proposed integrated model is evaluated across several real-world datasets to examine the performance and reliability of the proposed model through real X-ray images. This paper is organized as follows: Sections 2 and 3 describe the background, motivation, and literature review.