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 …

The recent release of large-scale healthcare datasets has greatly propelled the research of
data-driven deep learning models for healthcare applications. However, due to the nature of …

In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the
fairness of such data-driven insights remains a concern in high-stakes fields. Despite …

Machine learning models are increasingly adopted for facilitating clinical decision-making.
However, recent research has shown that machine learning techniques may result in …

A growing body of work uses the paradigm of algorithmic fairness to frame the development
of techniques to anticipate and proactively mitigate the introduction or exacerbation of health …

In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

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 …

Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes
applications such as those in healthcare. However, health AI models' overall prediction …

The emergence and increasing use of artificial intelligence and machine learning (AI/ML) in
healthcare practice and delivery is being greeted with both optimism and caution. We focus …