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 …

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 …

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

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 …

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 …

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 …

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 …

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

As machine learning methods gain prominence within clinical decision-making, the need to
address fairness concerns becomes increasingly urgent. Despite considerable work …