Application of Artificial Intelligence in Dentistry

Abstract

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.

Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry—A Systematic Review

“…AI models are bringing greater efficiency and accuracy, capitalizing on the interest, capabilities, and skills of those involved [ 48 ]. Effective and efficient interprofessional and clinician-patient interactions have evolved using these new ways, with AI students have new ways of learning through research and the data collected can be efficiently utilized for forensic and epidemiological uses [ 49 , 50 ]. Extensive research has been carried out on the application, benefits, and comparison of AI with human skills around the globe.…”

The Modern and Digital Transformation of Oral Health Care: A Mini Review

“…Diagnosis of temporomandibular joint disorders (TMDs) can be detected based primarily on history and signs and symptoms, which are then to be followed by clinical examinations. Theoretically, the collective clinical data of TMDs examinations, if they can be translated into a well-structured and organized computer language, are able to differentiate between absolute TMDs diagnosis and other clinical conditions mimicking TMDs [ 65 ]. A study by Shoukri et al (2019) showed that the neural networks are able to program and classify the TMDs based on the combination of condylar radiographic imaging utilizing CBCT, biological markers such as saliva, and a variable range of clinical indicators including detailed facial and muscle pain and soreness history, range of mouth opening, and other signs such as headaches [ 66 ].…”

“…The dental practitioner and the auxiliary teams are responsible for data handling with potential risks of data privacy and security breaches. The AI/VR systems are not held accountable for this, though the application is performed either under supervision or not within the legal and jurisprudence context [ 65 , 67 ].…”

“…The infrastructure support like computing power and requirements are critical to ensure the smooth processing of data updating, gathering, and interpretation in the delivery of the oral health care system which utilizes AI. The continuous expanding data of patients including demographic, clinical, treatment, and follow up requires consistent upgrading of the computing power which potentially may give rise to health, economic, training, technical logistics, and maintenance issues [ 65 , 69 ]. The inability to match the expected computational resources will reduce the efficacy of the AI, thus, reducing the delivery of the modern healthcare services.…”

Current Applications of Artificial Intelligence in Cleft Care: A Scoping Review

“…This will help improve decision-making in healthcare. The tasks for which this technology has been applied are of a preliminary nature ( 4 ). It has been used in medicine to aid diagnosis, interpretation of medical images, treatment planning, and prediction of treatment outcomes.…”