Artificial Intelligence in Dentistry: Literature Review

Abstract

Artificial Intelligence has exploded as a research subject in the 21st century as the hardware requirements of theoretical Artificial intelligence in the 19th and 20th century have translated into reality. This has led to rapid progress with the realization of multiple kinds of neural networks, as well as improvements along classical Machine Learning models like Decision Trees, Support Vector Machines etc. Most recently, the focus in Machine Learning has now shifted towards generative networks with Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) leading modern research topics.
 With advent of the Internet of Medical Things (IoMT) and modern time leaps in the domain of Artificial Intelligence with the explosion of Machine and Deep Learning, Medical AI has grasped the central stage in modern research. Applications of Medical AI vary from detections of various cancerous tumors to prediction of arrhythmia attacks. The augmentation of such AI embedded techniques into the medical profession has streamlined analysis as well as aided professionals in reaching a more efficient and accurate diagnosis. This has also translated in the field of dentistry where strong deep learning architectures such as Convolutional neural networks in the form of Resnet, Inception, GoogleNet etc have been used to process raw images and detect a range of dental diseases.
 This paper aims to provide an overview of progress made in the field of Machine Learning particularly focusing on its medical and dental applications. Different facets of Machine Learning are discussed with respect to their strengths, shortcomings, and the way artificial intelligence has been used to tackle problems in the medical field. Finally, a descriptive overview about state-of-the-art machine learning reliant applications that are being used in different dental subfields is discussed along with current challenges the industry faces today.

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