A growing number of artificial intelligence (AI)-based clinical decision support systems are
showing promising performance in preclinical, in silico, evaluation, but few have yet …

Translating machine learning (ML) models effectively to clinical practice requires establishing
clinicians’ trust. Explainability, or the ability of an ML model to justify its outcomes and …

Artificial intelligence has exposed pernicious bias within health data that constitutes substantial
ethical threat to the use of machine learning in medicine. 1, 2 Solutions of algorithmic …

Artificial intelligence as a medical device is increasingly being applied to healthcare for
diagnosis, risk stratification and resource allocation. However, a growing body of evidence has …

Background Human beings have long consumed opiates and opioids for pleasure and as a
treatment for numerous ailments, most notably pain. North America is currently in the grips of …

The application of artificial intelligence and machine learning (ML) technologies in healthcare
have immense potential to improve the care of patients. While there are some emerging …

Accumulating evidence demonstrates the impact of bias that reflects social inequality on the
performance of machine learning (ML) models in health care. Given their intended …

Familiar concepts from research ethics can guide the meaningful and rigorous translation of
artificial intelligence (AI) tools into clinical practice.

Background: As artificial intelligence (AI) approaches in research increase and AI becomes
more integrated into medicine, there is a need to understand perspectives from members of …

Importance Understanding the views and values of patients is of substantial importance to
developing the ethical parameters of artificial intelligence (AI) use in medicine. Thus far, there …