Artificial intelligence (AI) is a technology that utilizes machines to mimic intelligent human behavior. To appreciate human-technology interaction in the clinical setting, augmented intelligence has been proposed as a cognitive extension of AI in health care, emphasizing its assistive and supplementary role to medical professionals. While truly autonomous medical robotic systems are still beyond reach, the virtual component of AI, known as software-type algorithms, is the main component used in dentistry. Because of their powerful capabilities in data analysis, these virtual algorithms are expected to improve the accuracy and efficacy of dental diagnosis, provide visualized anatomic guidance for treatment, simulate and evaluate prospective results, and project the occurrence and prognosis of oral diseases. Potential obstacles in contemporary algorithms that prevent routine implementation of AI include the lack of data curation, sharing, and readability; the inability to illustrate the inner decision-making process; the insufficient power of classical computing; and the neglect of ethical principles in the design of AI frameworks. It is necessary to maintain a proactive attitude toward AI to ensure its affirmative development and promote human-technology rapport to revolutionize dental practice. The present review outlines the progress and potential dental applications of AI in medical-aided diagnosis, treatment, and disease prediction and discusses their data limitations, interpretability, computing power, and ethical considerations, as well as their impact on dentists, with the objective of creating a backdrop for future research in this rapidly expanding arena.

Application of Artificial Intelligence in Dentistry.

Background and aims Dentistry involves close face-to-face interaction with patients, hence during the COVID-19 pandemic, it has mostly been suspended. Teledentistry can offer an innovative solution to resume dental practice during this pandemic. In this review, we provide a brief overview of applications of teledentistry. Methods Articles on teledentistry, relevant to this review, were searched and consulted from PubMed, Google Scholar, and Cochrane database. Results Teledentistry is the remote facilitating of dental treatment, guidance, and education via the use of information technology instead of direct face-to-face contact with patients. Teleconsultation, telediagnosis, teletriage, and telemonitoring are subunits of teledentistry that have important functions relevant to dental practice. There are many challenges for acceptance of teledentistry by the dentists as well as patients, which need to be addressed urgently. Conclusion Teledentistry can offer a novel solution to resume dental practice during the current pandemic, hence, the need of the hour is to incorporate teledentistry into routine dental practice. If not fully replace, at least teledentistry can complement the existing compromised dental system during the current pandemic.

/pdf/teledentistry-during-covid-19-pandemic-4rad6z10sl.pdf

Teledentistry during COVID-19 pandemic.

Sudden spurting of Corona virus disease (COVID-19) has put the whole healthcare system on high alert. Internet of Medical Things (IoMT) has eased the situation to a great extent, also COVID-19 has motivated scientists to make new 'Smart' healthcare system focusing towards early diagnosis, prevention of spread, education and treatment and facilitate living in the new normal. This review aims to identify the role of IoMT applications in improving healthcare system and to analyze the status of research demonstrating effectiveness of IoMT benefits to the patient and healthcare system along with a brief insight into technologies supplementing IoMT and challenges faced in developing a smart healthcare system. An internet-based search in PUBMED, Google Scholar and IEEE Library for english language publications using relevant terms resulted in 987 articles. After screening title, abstract, and content related to IoMT in healthcare and excluding duplicate articles, 135 articles published in journal with impact factor ≥1 were eligible for inclusion. Also relevant articles from the references of the selected articles were considered. The habituation of IoMT and related technology has resolved several difficulties using remote monitoring, telemedicine, robotics, sensors etc. However mass adoption seems challenging due to factors like privacy and security of data, management of large amount of data, scalability and upgradation etc. Although ample knowledge has been compiled and exchanged, this structured systematic review will help the healthcare practitioners, policymakers/decision makers, scientists and researchers to gauge the applicability of IoMT in healthcare more efficiently. 

Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review.

A broad view is taken of the past, present, and future of the Radiological Society of North America. The Society's concerns are enumerated: concern with cooperation between radiologists and technologists and between radiologists and those in other branches of medicine and allied sciences; concern in providing leadership in the field of radiation measurements and protection; in support of research; in scientific education of radiologists. At the Scientific Assembly and Annual Meeting every year, the educational experience is four-pronged with its refresher courses, lectures, scientific exhibits, and technical exhibits.

The Radiological Society of North America, Inc.

Early detection of oral cancer necessitates a minimally invasive, tissue-specific diagnostic tool that facilitates screening/surveillance. Brush biopsy, though minimally invasive, demands skilled cyto-pathologist expertise. In this study, we explored the clinical utility/efficacy of a tele-cytology system in combination with Artificial Neural Network (ANN) based risk-stratification model for early detection of oral potentially malignant (OPML)/malignant lesion. A portable, automated tablet-based tele-cytology platform capable of digitization of cytology slides was evaluated for its efficacy in the detection of OPML/malignant lesions (n = 82) in comparison with conventional cytology and histology. Then, an image pre-processing algorithm was established to segregate cells, ANN was trained with images (n = 11,981) and a risk-stratification model developed. The specificity, sensitivity and accuracy of platform/ stratification model were computed, and agreement was examined using Kappa statistics. The tele-cytology platform, Cellscope, showed an overall accuracy of 84-86% with no difference between tele-cytology and conventional cytology in detection of oral lesions (kappa, 0.67-0.72). However, OPML could be detected with low sensitivity (18%) in accordance with the limitations of conventional cytology. The integration of image processing and development of an ANN-based risk stratification model improved the detection sensitivity of malignant lesions (93%) and high grade OPML (73%), thereby increasing the overall accuracy by 30%. Tele-cytology integrated with the risk stratification model, a novel strategy established in this study, can be an invaluable Point-of-Care (PoC) tool for early detection/screening in oral cancer. This study hence establishes the applicability of tele-cytology for accurate, remote diagnosis and use of automated ANN-based analysis in improving its efficacy.

/pdf/a-smart-tele-cytology-point-of-care-platform-for-oral-cancer-4mjs5ufoj3.pdf

A smart tele-cytology point-of-care platform for oral cancer screening.

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A smart tele-cytology point-of-care platform for oral cancer screening.