Equitable and fair AI for health care requires a team effort
KAREN DRUKKER AND MARYELLEN GIGER, UNIVERSITY OF CHICAGO
The potential for bias and unfairness in the integration of AI in health care-centered research and diagnostics, particularly in medical image analysis, must be addressed to ensure equitable and effective outcomes for all patients. Biases can occur during the implementation of any of the five steps of the imaging AI model development pipeline, as our Medical Imaging and Data Resource Center (MIDRC) team from the University of Chicago and other institutions has pointed out. These steps include data collection, data preparation and annotation, model development, model evaluation, and model...Read full article
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Published: July 2023