Research Guides: Health Equity: AI and Health Equity

A.I. Learning Resources

Exclusion cycles: Reinforcing disparities in medicine

Bracic, Ana ; Callier, Shawneequa L. ; Price, W. Nicholson. (n.d.). Exclusion cycles: Reinforcing disparities in medicine. Science (American Association for the Advancement of Science). Washington: The American Association for the Advancement of Science.

Artificial Intelligence in Healthcare: The Perspectives of Medical Students and Health Professionals

de Bessa, João Pedro Alves. (n.d.). Artificial Intelligence in Healthcare: The Perspectives of Medical Students and Health Professionals. ProQuest Dissertations Publishing.

How Health Care Algorithms and AI Can Help and Harm
Johns Hopkins Bloomberg School of Public Health. Algorithms can amplify health inequities—or help find biases in medical data.
U-M: Training for Research and Instruction
Generative AI capabilities are wide-ranging and will impact departments across campus in unique ways. For the most relevant guidance and training resources, we recommend first contacting your unit, as they may be able to assist you in accessing training material specifically curated for your area.

Health Equity

A.I. and Health Equity: helpful or harmful?

A.I. Learning Resources

A.I. Resources at U-M