Navigating fairness aspects of clinical prediction models.

ABSTRACT:

Background

Algorithms are increasingly used in healthcare, yet most algorithms lack thorough evaluation and impact assessment across diverse populations. This absence of comprehensive scrutiny introduces a significant risk of inequitable clinical outcomes, particularly between different demographic and socioeconomic groups.

Main body

Societal biases-rooted in structural inequalities and systemic discrimination-often shape the data used to develop these algorithms. When such biases become embedded into predictive models, algorithms frequently favor privileged populations, further deepening existing inequalities. Without proactive efforts to identify and mitigate these biases, algorithms risk disproportionately harming already marginalized groups, widening the gap between adva

SUBMITTER: Chakradeo K 

PROVIDER: S-EPMC12535043 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature