Background. Dental caries is one of the major oral health problems and is increasing rapidly among people of every age (children, men, and women). Deep learning, a field of Artificial Intelligence (AI), is a growing field nowadays and is commonly used in dentistry. AI is a reliable platform to make dental care better, smoother, and time-saving for professionals. AI helps the dentistry professionals to fulfil demands of patients and to ensure quality treatment and better oral health care. AI can also help in predicting failures of clinical cases and gives reliable solutions. In this way, it helps in reducing morbidity ratio and increasing quality treatment of dental problem in population. Objectives. The main objective of this study is to conduct a systematic review of studies concerning the association between dental caries and machine learning. The objective of this study is to design according to the PICO criteria. Materials and Methods. A systematic search for randomized trials was conducted under the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this study, e-search was conducted from four databases including PubMed, IEEE Xplore, Science Direct, and Google Scholar, and it involved studies from year 2008 to 2022. Result. This study fetched a total of 133 articles, from which twelve are selected for this systematic review. We analyzed different types of machine learning algorithms from which deep learning is widely used with dental caries images dataset. Neural Network Backpropagation algorithm, one of the deep learning algorithms, gives a maximum accuracy of 99%. Conclusion. In this systematic review, we concluded how deep learning has been applied to the images of teeth to diagnose the detection of dental caries with its three types (proximal, occlusal, and root caries). Considering our findings, further well-designed studies are needed to demonstrate the diagnosis of further types of dental caries that are based on progression (chronic, acute, and arrested), which tells us about the severity of caries, virginity of lesion, and extent of caries. Apart from dental caries, AI in the future will emerge as supreme technology to detect other diseases of oral region combinedly and comprehensively because AI will easily analyze big datasets that contain multiple records.

After skin cancer, the most common type of cancer is breast cancer among the world population. Breast cancer is the leading cause of cancer-induced mortality among women. Breast cancer is frequently diagnosed by using biopsies in which tissue is removed from the breast and studied under a microscope. The results of these biopsies are based on the qualifications and experience of the pathologist who diagnoses the abnormal cell under the microscope. With the emergence of advancements in the fields of image processing and artificial intelligence, there is an area of interest in developing a deep learning model to improve and enhance the quality and accuracy of breast cancer diagnosis. This study proposed a deep learning model that automatically analyses the multiclass classification of hematoxylin and eosin-stained histological images of invasive ductal carcinoma (IDC) by discriminating the IDC into grades such as G-1, G-2, and G-3. The methodology is focused on a deep learning model to detect grades of invasive ductal carcinoma by adopting the Sequential Convolutional Neural Network Two-Dimensional (CNN2D). We used DataBiox, a public dataset taken from an internet source consisting of 922 images. We evaluate the result of multiclass classification by dividing 80% and 20% of the dataset into training and testing data, respectively. As a result of the training and testing of the pre-trained CNN model, sequential CNN yields the accuracy of the model of 92.81%. Our model accurately classifies a multi-class classification of histological images of grades of breast cancer, specifically IDC. It is ready to be tested with a more diverse and massive database in the future.