BACKGROUND: Oral mucosal lesions are widespread globally, have a high prevalence in clinical practice, and significantly impact patients' quality of life. However, their pathogenesis remains unclear. Recent evidences suggested that hematological para...
The classification of intraoral teeth structures is a critical component in modern dental analysis and forensic dentistry. Traditional methods, relying on 2D imaging, often suffer from limitations in accuracy and comprehensiveness due to the complex ...
BACKGROUND: Artificial intelligence (AI) chatbots are excellent at generating language. The growing use of generative AI large language models (LLMs) in healthcare and dentistry, including endodontics, raises questions about their accuracy. The poten...
OBJECTIVE: Artificial intelligence (AI) has been widely used in various medical fields to support diagnostic development. The development of different AI techniques has made important contributions to early diagnoses. This research compares and evalu...
BACKGROUND: Digital cephalometric analyses, including those assisted by artificial intelligence (AI), are widely used in clinical practice. Similarly, computer-assisted learning has demonstrated teaching outcomes comparable to those of traditional me...
BACKGROUND: Dental anxiety is a pervasive problem worldwide, leading to avoidance of dental care, resulting in oral health problems and impacting daily life through social withdrawal and work absenteeism. Addressing this fear is an important public h...
BACKGROUND: Artificial intelligence (AI) has rapidly advanced in healthcare and dental education, significantly impacting diagnostic processes, treatment planning, and academic training. The aim of this study is to evaluate the performance difference...
BACKGROUND: Artificial intelligence (AI) chatbots are increasingly used in healthcare to address patient questions by providing personalized responses. Evaluating their performance is essential to ensure their reliability. This study aimed to assess ...
OBJECTIVE: This study evaluates the performance of various classifiers and pre-trained models for dental implant state classification using preprocessed radiography images with masks.