A scoping review and evidence gap analysis of clinical AI fairness.

ABSTRACT: The ethical integration of artificial intelligence (AI) in healthcare necessitates addressing fairness. AI fairness involves mitigating biases in AI and leveraging AI to promote equity. Despite advancements, significant disconnects persist between technical solutions and clinical applications. Through evidence gap analysis, this review systematically pinpoints the gaps at the intersection of healthcare contexts-including medical fields, healthcare datasets, and bias-relevant attributes (e.g., gender/sex)-and AI fairness techniques for bias detection, evaluation, and mitigation. We highlight the scarcity of AI fairness research in medical domains, the narrow focus on bias-relevant attributes, the dominance of group fairness centering on model performance equality, and the limited integratio

SUBMITTER: Liu M 

PROVIDER: S-EPMC12167363 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature