Accumulating evidence demonstrates the impact of bias that reflects social inequality on the
performance of machine learning (ML) models in health care. Given their intended …

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 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 use of machine learning (ML) in healthcare raises numerous ethical concerns,
especially as models can amplify existing health inequities. Here, we outline ethical …

Machine learning is used increasingly in clinical care to improve diagnosis, treatment
selection, and health system efficiency. Because machine-learning models learn from …

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 application of artificial intelligence and machine learning (ML) technologies in
healthcare have immense potential to improve the care of patients. While there are some …

Health equity is a primary goal of healthcare stakeholders: patients and their advocacy
groups, clinicians, other providers and their professional societies, bioethicists, payors and …

Machine learning (ML) systems play an increasingly relevant role in medicine and
healthcare. As their applications move ever closer to patient care and cure in clinical …