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 …

Objectives To summarise the accuracy of artificial intelligence (AI) computer vision
algorithms to classify ear disease from otoscopy. Design Systematic review and meta …

Background Otologic diseases are often difficult to diagnose accurately for primary care
providers. Deep learning methods have been applied with great success in many areas of …

This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of
otorhinolaryngology, considering opportunities, research challenges, and research …

Background Ear and mastoid disease can easily be treated by early detection and
appropriate medical care. However, short of specialists and relatively low diagnostic …

Objectives/Hypothesis To create a new strategy for monitoring pediatric otitis media (OM),
we developed a brief, reliable, and objective method for automated classification using …

We propose a novel automated algorithm for classifying diagnostic categories of otitis
media: acute otitis media, otitis media with effusion, and no effusion. Acute otitis media …

In medicine, a misdiagnosis or the absence of specialists can affect the patient's health,
leading to unnecessary tests and increasing the costs of healthcare. In particular, the lack of …

Telehealth in otolaryngology is gaining popularity as a potential tool for increased access for
rural populations, decreased specialist wait times, and overall savings to the healthcare …

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 …