Effective evaluation and governance of predictive models used in health care, particularly those driven by artificial intelligence (AI) and machine learning, are needed to ensure that models are fair, ...

This study compared 6 algorithmic fairness–improving approaches for low-birth-weight predictive models and found that they improved accuracy but decreased sensitivity for Black populations. Objective: ...

Many U.S. hospitals using predictive models are not evaluating their tools internally for accuracy, and fewer still are evaluating them for potential biases, according to a study published in the most ...

Algorithms have a critical role to play in population health management. Through a collective process of ensuring that these algorithms are constructed and applied fairly, we can ensure these benefits ...

Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and ...