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Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review
0
Zitationen
3
Autoren
2025
Jahr
Abstract
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.
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