Recent advancements in deep learning and artificial intelligence (AI) have profoundly
impacted various fields, including diagnostic imaging. Integrating AI technologies such as …

With the increasing development of artificial intelligence (AI) and deep learning (DL)
technology, the application of DL networks in medical image analysis has attracted more …

Deep learning is a form of machine learning using a convolutional neural network
architecture that shows tremendous promise for imaging applications. It is increasingly being …

Objective To provide a state of the art review of artificial intelligence (AI), including its
subfields of machine learning and natural language processing, as it applies to …

The amount of data available within healthcare has skyrocketed over the last several
decades from the introduction of electronic medical record system and healthcare …

Objective Access to otolaryngology is limited by lengthy wait lists and lack of specialists,
especially in rural and remote areas. The objective of this study was to use an automated …

Introduction Artificial intelligence models and networks can learn and process dense
information in a short time, leading to an efficient, objective, and accurate clinical and …

Simple Summary Deep learning techniques have significant potential in head and neck
cancer imaging, particularly in tumor detection, segmentation, and outcome prediction using …

Otolaryngology stands on the precipice of an era of innovation, poised to integrate Artificial
Intelligence (AI) and chatbots into its research framework. AI has the potential to use vast …

Deep learning (DL) is one of the branches of artificial intelligence that has seen exponential
growth in recent years. The scientific community has focused its attention on DL due to its …