Who Gets Health Care and Why: AI, Race and Health Equity
Time & Location
Rapid advances in artificial intelligence (AI) are transforming the way physicians and hospitals view and provide medical care. Yet, the latest evidence suggests the common practice of race-correction in clinical AI often exacerbates longstanding inequities in health outcomes and the type of health care received by Black Americans, Latinos, Asian Americans, and other medically underserved groups.
Algorithms that correct for race are currently used to inform treatment for more than thirteen specialties, including obstetrics, cardiology, nephrology, and oncology, even though race has not been proven to be a reliable indicator of genetic differences.
For this NIHCM webinar, leading researchers in the field will explore:
- The scope and likely impact of clinical AI on health disparities and guidelines for considering race in diagnostic algorithms.
- How we can design clinical AI algorithms that can improve health equity and reduce the impact of systemic racism on health.
- The role that health care organizations and health plans can play in creating AI that supports health equity.
David S. Jones, MD, PhD
Ackerman Professor of the Culture of Medicine, Harvard University
Fay Cobb Payton, PhD
Professor of Information Technology/Analytics at North Carolina State University
SVP & Chief Digital Officer, Anthem, Inc.
This webinar is the fifth part of the series "Stopping the Other Pandemic: Systemic Racism and Health."
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