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 …

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 …

The machine learning community has become alert to the ways that predictive algorithms
can inadvertently introduce unfairness in decision-making. Herein, we discuss how concepts …

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 …

Bias in applications of machine learning (ML) to healthcare is usually attributed to
unrepresentative or incomplete data, or to underlying health disparities. This article identifies …

Objective The integration of artificial intelligence (AI) and machine learning (ML) in health
care to aid clinical decisions is widespread. However, as AI and ML take important roles in …

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

Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD)
recommend the use of risk stratification models to identify patients most likely to benefit from …

While interest in the application of machine learning to improve healthcare has grown
tremendously in recent years, a number of barriers prevent deployment in medical practice …

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 …