Online videos are vital for health education and medical decision-making, but their comment sections often spread misinformation, causing anxiety and confusion. This study identifies stress-inducing comments in oral health education content, aiming t...
This research introduces Oral Score Basic (OS-B), a novel Artificial Intelligence (AI) derived methodology designed to provide a comprehensive, objective assessment of individual teeth and overall oral health, initially focused on dental conditions. ...
BACKGROUND: Early childhood caries (ECC) is a widespread pediatric dental condition that is influenced by a combination of biological, behavioral, and demographic factors. Salivary biomarkers, including beta-defensin-2 (BD-2) and statherin (STATH), o...
This empirical study assessed the potential of developing a machine-learning model to identify children and adolescents with poor oral health using only self-reported survey data. Such a model could enable scalable and cost-effective screening and ta...
OBJECTIVES: This study aimed to develop a model to predict the autonomy preference (AP) and satisfaction after tooth extraction (STE) in patients with periodontal disease. Understanding of individual AP and STE is essential for improving patient sati...
BACKGROUND: Recent studies have identified significant gaps in equity, diversity, and inclusion (EDI) considerations within the lifecycle of artificial intelligence (AI), spanning from data collection and problem definition to implementation stages. ...
PURPOSE: With the increasing use of artificial intelligence (AI) in dentistry, it is feasible to self-monitor oral health using Oral Health AI Advisors (OHAI Advisors). This technological advancement offers the potential for early detection of oral d...
OBJECTIVES: Maintenance of oral muscle functions is important for survival and communication. Utilizing Artificial Intelligence (AI) as a self-health-management material has shown promise. Here we developed a functional and AI-enabled smartphone e-Or...
BACKGROUND: Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligenc...
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