Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …

Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …

Introduction The aim of this systematic review and meta-analysis was to investigate the
overall accuracy of deep learning models in detecting periapical (PA) radiolucent lesions in …

Medical imaging plays a crucial role in diagnosis, with radiology reports serving as vital
documentation. Automating report generation has emerged as a critical need to alleviate the …

Deep Learning (DL) has the potential to optimize machine learning in both the scientific and
clinical communities. However, greater expertise is required to develop DL algorithms, and …

Due to the necessity for precise treatment planning, the use of panoramic X-rays to identify
different dental diseases has tremendously increased. Although numerous ML models have …

Purpose Convolutional neural networks (CNNs) have rapidly emerged as one of the most
promising artificial intelligence methods in the field of medical and dental research. CNNs …

This study aims to evaluate the diagnostic accuracy of artificial intelligence in detecting
apical pathosis on periapical radiographs. A total of twenty anonymized periapical …

Objective: In this paper, we aimed to evaluate the performance of a deep learning system for
automated tooth detection and numbering on pediatric panoramic radiographs. Study …

Objective This systematic review aimed to summarize and evaluate the available information
regarding the performance of artificial intelligence on dental implant classification and peri …