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Decoding Algorithmic Inequity: Reversing the Generative AI Racial Divide in Healthcare
Jaysón Davidson1, Ibukun Fowe2, Abosede Oderinde3
1DataTecnica Inc., Washington, District of Columbia.
Clinical Therapeutics
|June 29, 2026
Summary
Healthcare AI systems amplify existing biases, leading to health disparities for underserved populations. Addressing this requires a multimodal framework focusing on data governance, transparency, and community involvement to ensure equitable AI in global health.
Area of Science:
- Health Informatics
- Artificial Intelligence in Medicine
- Health Equity Research
Background:
- Healthcare AI systems increasingly demonstrate inherited, operationalized, and amplified biases and structural inequities.
- These biases disproportionately affect marginalized and underserved populations, impacting healthcare access and outcomes.
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