Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review

Abstract

This study systematically evaluates how existing AI frameworks in healthcare address intersectional bias across the AI lifecycle and explores the mitigation strategies proposed. Scoping review. A scoping review was conducted per PRISMA-ScR guidelines, analyzing studies from 2014-2024. 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). 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. 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. This paper examines fairness and inequality in healthcare AI, which could improve diagnosis and treatment but may worsen existing disparities. A review of 43 studies reveals that many AI frameworks inadequately address “intersectional bias”—how combined factors like gender, race, and economic status create unique discrimination. Many frameworks overlook factors like disability and citizenship, risking unfair AI systems and unequal care. The paper identifies common AI development biases (e.g., unrepresentative datasets, data labelling errors) and suggests solutions: diverse data collection, fairness checks, and clear guidelines. The authors stress the need for fair, inclusive AI to prevent increased health disparities, and offer recommendations for developers, policymakers, and healthcare providers.

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