Machine learning is expected to fuel significant improvements in medical care. To ensure
that fundamental principles such as beneficence, respect for human autonomy, prevention of …

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 …

Past literature has been effective in demonstrating ideological gaps in machine learning
(ML) fairness definitions when considering their use in complex socio-technical systems …

The use of Artificial Intelligence (AI) is on track to revolutionize healthcare, with performance
in medical tasks such as clinical diagnosis often being comparable to expert-level accuracy …

This paper is one of the first to analyse the ethical implications of specific healthcare artificial
intelligence (AI) applications, and the first to provide a detailed analysis of AI‐based systems …

Benefiting from the digitization of healthcare data and the development of computing power,
machine learning methods are increasingly used in the healthcare domain. Fairness …

The field of mobile, wearable, and ubiquitous computing (UbiComp) is undergoing a
revolutionary integration of machine learning. Devices can now diagnose diseases, predict …

There are many incompatible ways to measure fair outcomes for machine learning
algorithms. The goal of this paper is to characterize rates of success and error across …

Artificial intelligence (AI) and its machine learning (ML) algorithms are offering new promise
for personalized biomedicine and more cost-effective healthcare with impressive technical …

The use of artificial intelligence (AI) in healthcare has the potential to improve patient
outcomes and increase efficiency in the delivery of care. However, the design, deployment …