Algorithmic individual fairness and healthcare: a scoping review.

ABSTRACT:

Objectives

Statistical and artificial intelligence algorithms are increasingly being developed for use in healthcare. These algorithms may reflect biases that magnify disparities in clinical care, and there is a growing need for understanding how algorithmic biases can be mitigated in pursuit of algorithmic fairness. We conducted a scoping review on algorithmic individual fairness (IF) to understand the current state of research in the metrics and methods developed to achieve IF and their applications in healthcare.

Materials and methods

We searched four databases: PubMed, ACM Digital Library, IEEE Xplore, and medRxiv for algorithmic IF metrics, algorithmic bias mitigation, and healthcare applications. Our search was restricted to articles published between January 2013 and

SUBMITTER: Anderson JW 

PROVIDER: S-EPMC11684587 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature