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 …

The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such
algorithms may be shaped by various factors such as social determinants of health that can …

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 …

Population and public health are in the midst of an artificial intelligence revolution capable of
radically altering existing models of care delivery and practice. Just as AI seeks to mirror …

Medicine is at a disciplinary crossroads. With the rapid integration of Artificial Intelligence
(AI) into the healthcare field the future care of our patients will depend on the decisions we …

With the growing utilization of machine learning in healthcare, there is increasing potential to
enhance healthcare outcomes. However, this also brings the risk of perpetuating biases in …

Abstract An Artificial Intelligence algorithm trained on data that reflect racial biases may yield
racially biased outputs, even if the algorithm on its own is unbiased. For example, algorithms …

Artificial intelligence (AI) shows promise for improving basic and translational science,
medicine, and public health, but its success is not guaranteed. Numerous examples have …

Artificial intelligence holds great promise for improved health‐care outcomes. But it also
poses substantial new hazards, including algorithmic discrimination. For example, an …

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