A Comparative Analysis of Three Large Language Models on Bruxism Knowledge.

Background: Artificial Intelligence (AI) has been widely used in health research, but the effectiveness of large language models (LLMs) in providing accurate information on bruxism has not yet been evaluated.

Objectives: To assess the readability, accuracy and consistency of three LLMs in responding to frequently asked questions about bruxism.

Methods: This cross-sectional observational study utilised the Google Trends tool to identify the 10 most frequent topics about bruxism.

Sex determination through maxillary dental arch and skeletal base measurements using machine learning.

Background: Cranial, facial, nasal, and maxillary widths have been shown to be significantly affected by the individual's sex. The present study aims to use measurements of dental arch and maxillary skeletal base to determine sex, employing supervised machine learning.

Materials And Methods: Maxillary and mandibular tomographic examinations from 100 patients were analyzed to investigate the inter-premolar width, inter-molar width, maxillary width, inter-pterygoid width, nasal cavity width, nostril width, and maxillary length, obtained through Cone Beam Computed Tomography scans.