Objective Data-driven population segmentation is commonly used in clinical settings to
separate the heterogeneous population into multiple relatively homogenous groups with …

Purpose: To systematically evaluate artificial intelligence applications for diagnostic and
treatment planning possibilities in pediatric dentistry. Methods: PubMed®, EMBASE® …

Modern education relies heavily on educational technology, which provides students with
unique learning experiences and enhances their ability to learn. There has been a long …

In this study, we developed machine learning-based prediction models for early childhood
caries and compared their performances with the traditional regression model. We analyzed …

This study introduces a simple yet effective method for identifying similar data points across
non-free text domains, such as tabular and image data, using Large Language Models …

The human oral cavity harbors one of the most diverse microbial communities with different
oral microenvironments allowing the colonization of unique microbial species. This study …

Background: Machine learning (ML) has potential to assist dental professionals with
diagnosing and predicting outcomes of oral health. Tooth decay in children is the most …

Climate change, particularly sea level rise, poses significant challenges to global coastal
regions, necessitating a profound understanding of their vulnerability for effective mitigation …

Importance Children with special health care needs (CSHCN) are recognized to be at
increased risk of developing dental caries (decay). Evidence is mixed regarding the …

Objectives: To evaluate how different data sources affect the performance of machine
learning algorithms that predict dental general anesthesia use among children with …