The effectiveness of using artificial intelligence (AI) systems to perform diabetic retinal
exams ('screening') on preventing vision loss is not known. We designed the Care Process …

Several principles have been proposed to improve use of artificial intelligence (AI) in
healthcare, but the need for AI to improve longstanding healthcare challenges has not been …

Abstract Objective Artificial Intelligence (AI) refers to the ability of machines to perform
cognitive and intellectual human tasks. In dentistry, AI offers the potential to enhance …

Bias in textual data can lead to skewed interpretations and outcomes when the data is used.
These biases could perpetuate stereotypes, discrimination, or other forms of unfair …

The advent of artificial intelligence (AI) and machine learning (ML) has revolutionized the
field of medicine. Although highly effective, the rapid expansion of this technology has …

Objective: Clinical applications of machine learning are promising as a tool to improve
patient outcomes through assisting diagnoses, treatment, and analyzing risk factors for …

Background: Approximately 1 in 3 Canadians will experience an addiction or mental health
challenge at some point in their lifetime. Unfortunately, there are multiple barriers to …

This review delves into the most recent advancements in applying artificial intelligence (AI)
within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors …

Objective: Clinical data's confidential nature often limits the development of machine
learning models in healthcare. Generative adversarial networks (GANs) can synthesise …

Artificial Intelligence (AI) for health has a great potential; it has already proven to be
successful in enhancing patient outcomes, facilitating professional work and benefiting …