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 …

While the opportunities of ML and AI in healthcare are promising, the growth of complex data-
driven prediction models requires careful quality and applicability assessment before they …

Abstract Machine Learning (ML) applications are making a considerable impact on
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …

A variety of ethical concerns about artificial intelligence (AI) implementation in healthcare
have emerged as AI becomes increasingly applicable and technologically advanced. The …

Introduction Recent developments in the field of Artificial Intelligence (AI) applied to
healthcare promise to solve many of the existing global issues in advancing human health …

Social stereotypes negatively impact individuals' judgments about different groups and may
have a critical role in understanding language directed toward marginalized groups. Here …

Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific
challenges of our time and is key to increase the adoption of Artificial Intelligence (AI) …

The use of machine learning to guide clinical decision making has the potential to worsen
existing health disparities. Several recent works frame the problem as that of algorithmic …

The problem of algorithmic bias represents an ethical threat to the fair treatment of patients
when their care involves machine learning (ML) models informing clinical decision-making …

Importance Health care algorithms are used for diagnosis, treatment, prognosis, risk
stratification, and allocation of resources. Bias in the development and use of algorithms can …