Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review Author links open overlay panel

Abstract

Objectives: This study systematically evaluates how existing AI frameworks in healthcare address intersectional bias across the AI lifecycle and explores the mitigation strategies proposed. Study design: Scoping review. Methods: A scoping review was conducted per PRISMA-ScR guidelines, analyzing studies from 2014 to 2024. Searches included MEDLINE (Ovid), PubMed, EMBASE (Ovid), SCOPUS, ESCI, IEEE Xplore, and Google Scholar. Data were extracted on bias-related challenges and mitigation strategies across AI lifecycle phases (development, validation, implementation, monitoring). Studies were ranked by inclusivity (high, medium, or low). Results: Of 374 records, 43 studies met inclusion criteria, primarily from high-income countries. Gender/sex (51.2 %) and race/ethnicity (44.2 %) were the most addressed dimensions, while disability (14 %) and citizenship (9.3 %) were least addressed. Inclusivity was categorized as high (21 studies, 48.8 %), medium (23.2 %), or low (27.9 %). Overall, 14 biases and 21 mitigation strategies were identified. Conclusions Significant gaps remain in addressing intersectional biases in AI frameworks, particularly for underrepresented groups such as individuals with disabilities and non-citizens. Despite many frameworks demonstrating efforts toward inclusivity, attention to intersectionality remains uneven and largely inconsistent. Mapping biases to lifecycle phases highlights actionable strategies to improve equity and inclusivity in AI-driven healthcare. These findings provide valuable guidance for researchers, policymakers, and developers to create equitable and responsible AI systems.