Abstract Machine learning (ML) holds great promise for improving healthcare, but it is critical
to ensure that its use will not propagate or amplify health disparities. An important step is to …

In this review, we address the issue of fairness in the clinical integration of artificial
intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a …

Artificial intelligence (AI) has an astonishing potential in assisting clinical decision making
and revolutionizing the field of health care. A major open challenge that AI will need to …

Across machine learning (ML) sub-disciplines, researchers make explicit mathematical
assumptions in order to facilitate proof-writing. We note that, specifically in the area of …

As artificial intelligence (AI) makes continuous progress to improve quality of care for some
patients by leveraging ever increasing amounts of digital health data, others are left behind …

In a world of daily emerging scientific inquisition and discovery, the prolific launch of
machine learning across industries comes to little surprise for those familiar with the …

Abstract Machine learning algorithms can now identify patterns and correlations in (big)
datasets and predict outcomes based on the identified patterns and correlations. They can …

The significant advancements in applying artificial intelligence (AI) to healthcare decision-
making, medical diagnosis, and other domains have simultaneously raised concerns about …

The increasing implementation of and reliance on machine-learning (ML) algorithms to
perform tasks, deliver services and make decisions in health and healthcare have made the …

There is growing concern that decision-making informed by machine learning (ML)
algorithms may unfairly discriminate based on personal demographic attributes, such as …