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 …

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 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 …

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 …

Abstract Introduction Artificial Intelligence (AI) technology have been developed significantly
in recent years. The fairness of medical AI is of great concern due to its direct relation to …

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 …

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 …

Objectives Fairness is a core concept meant to grapple with different forms of discrimination
and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet …

Recent work has raised concerns on the risk of unintended bias in AI systems being used
nowadays that can affect individuals unfairly based on race, gender or religion, among other …