Artificial intelligence (AI) shows promise for improving basic and translational science, medicine, and public health, but its success is not guaranteed. Numerous examples have arisen of racial, ethnic, gender, disability, and other biases in AI applications to health care.

Co-authors Matthew DeCamp, MD, PhD and Charlotta Lindvall, MD, PhD write that ensuring equity will require more than unbiased data and algorithms. It will also require reducing biases in how clinicians and patients use AI-based algorithms—a potentially more challenging task than reducing biases in algorithms themselves.  Read article>>