AI has been part of our imaginations and simmering in research labs since a handful of computer scientists rallied around the term at a Research Workshop at Dartmouth College in 1956 and birthed the field of AI. Back in 1956, the dream of AI pioneers such as John McCarthy was to construct complex machines that possessed characteristics of human intelligence. However, general AI machines that replicate human senses, human reasoning, and think as we do are still mostly constrained to Hollywood and science fiction novels. AI today is, however, able to perform specific, comparably narrow tasks as well as, or sometimes better than, we humans can. Examples of narrow AI include applications such as classification of pathology from X-ray imagery, identification of people in Facebook photos via facial recognition, or your spam filters in Gmail.

Clove Dental offers a comprehensive set of oral healthcare services, leverages best-in-class equipment, and utilizes the latest pain-management technology to provide affordable healthcare of the highest quality. To establish itself as the industry leader, Clove adheres to the highest standards in clinic safety and hygiene, customer service, and recruiting, with a constant focus on ethics and transparency. Vikas Sood is the Chief Information Officer at Clove Dental. In an interaction with The Tech Pod, Vikas speaks about the future of AI in healthcare. Tell us something about yourself and what does your company do?

Align Technology's Chief Digital Officer Sreelakshmi Kolli is using machine learning and DevOps tactics to power the company's digital transformation.

Modeling eye movement indicative of expertise behavior is decisive in user evaluation. However, it is indisputable that task semantics affect gaze behavior. We present a novel approach to gaze scanpath comparison that incorporates convolutional neural networks (CNN) to process scene information at the fixation level. Image patches linked to respective fixations are used as input for a CNN and the resulting feature vectors provide the temporal and spatial gaze information necessary for scanpath similarity comparison.We evaluated our proposed approach on gaze data from expert and novice dentists interpreting dental radiographs using a local alignment similarity score. Our approach was capable of distinguishing experts from novices with 93% accuracy while incorporating the image semantics. Moreover, our scanpath comparison using image patch features has the potential to incorporate task semantics from a variety of tasks

Know Your Stuff is a new column that unlocks the hidden secrets about the everyday products you own. Dental care has come a long way since we were first using bone and hog hair brushes in sixth-century China, but based on some of the raised eyebrows I've seen at the recent CES electronics show, some might argue that the pendulum has swung too far in the other direction. Oral-B and Colgate, two household names in oral hygiene, each released state-of-the-art toothbrushes that promise to get your teeth cleaner than a standard brush. They join the ranks of dozens of other "smart brushes" that sport a list of features rivaling some laptops, which of course begs the question, "Why?" Aren't we fine with toothbrushes as they already are? Vision of the future:Is your eye the next frontier for small screen tech?

Overjet is an early-stage VC-backed startup building the future of data-driven dentistry. We are using AI to transform the $130B dental care market and improve patient outcomes. We are seeking an entrepreneurially-minded a highly skilled developer who is comfortable with backend software development including deploying machine learning models, loves challenges and is passionate about impacting lives. Please email your resume to careers@overjet.ai. Develop machine learning pipelines Deploy machine learning models for inference Implement and maintain metrics for tracking ML models performance Design and develop microservices and APIs related to data ingestion, machine learning and product quality Ensuring responsiveness of applications.

AI in healthcare is now playing a life-sustaining role helping people to get the accurate treatment with timely diagnosis of various types of diseases. Similarly, machine learning in healthcare is becoming more imperative covering more types of disorders in the body helping people take precautions and well- timed treatments. Machine Learning (ML) in dentistry for dental image analysis is playing an important role to find out the conditions of teeth helping 2. doctors to recommend the right treatment. But there is more improvements required in this sub-field of healthcare sector. Actually, machine learning algorithms is lying under the hood of high-quality medical training data sets, and with further advances in parallel computing and augmentation of training data sets ML will improve the dental image analysis.

Artificial Intelligence (AI) has increasingly become a dentists' best friend in improving productivity and make the best out of the $33 billion global dentistry market. Though machines are still learning to address certain dental anomalies creatively, the market is already blooming with AI-powered dental diagnostics products. It is projected that the AI tools, as of now can increase clinics' revenue by 25%. This could happen as a result of software program enhancing the quality of chair time of dentists by slicing down the time wasted on analyzing reports. Cavity or caries is among the most common dental issues.

DUBLIN--(BUSINESS WIRE)--The "Innovations in Antimicrobal Dental Fillings, Robotic Actuators and Grippers, Single-photon Emitters, Artificial Intelligence, and Autofocal Lenses" report has been added to ResearchAndMarkets.com's offering. The edition also provides insights on the role of single-photon emitters, artificial intelligence, and auto focal lenses. The TOE covers innovations in corrosion identification, Alzheimer's disease detection, and chemical separation methods. The TOE has comprehensive coverage on applications of AI and deep neural networks. Some of the topics covered include fuel cells and ultrathin wearable devices.

Discovering oral cavity cancer (OCC) at an early stage is an effective way to increase patient survival rate. However, current initial screening process is done manually and is expensive for the average individual, especially in developing countries worldwide. This problem is further compounded due to the lack of specialists in such areas. Automating the initial screening process using artificial intelligence (AI) to detect pre-cancerous lesions can prove to be an effective and inexpensive technique that would allow patients to be triaged accordingly to receive appropriate clinical management. In this study, we have applied and evaluated the efficacy of six deep convolutional neural network (DCNN) models using transfer learning, for identifying pre-cancerous tongue lesions directly using a small data set of clinically annotated photographic images to diagnose early signs of OCC. DCNN model based on Vgg19 architecture was able to differentiate between benign and pre-cancerous tongue lesions with a mean classification accuracy of 0.98, sensitivity 0.89 and specificity 0.97. Additionally, the ResNet50 DCNN model was able to distinguish between five types of tongue lesions i.e. hairy tongue, fissured tongue, geographic tongue, strawberry tongue and oral hairy leukoplakia with a mean classification accuracy of 0.97. Preliminary results using an (AI+Physician) ensemble model demonstrate that an automated initial screening process of tongue lesions using DCNNs can achieve near-human level classification performance for diagnosing early signs of OCC in patients.