How bias can creep into medical databanks that drive precision health and clinical AI
In a new Health Affairs paper, Kayter Spector-Bagdady, JD, MBioethics and coauthors explore the reasons for racial and ethnic disparities in research biospecimen and data bank recruitment and enrollment at a major academic medical center, Michigan Medicine.
In the race to harness medical data for artificial intelligence tools and personalized health care, this new study shows how easily unintentional design bias can affect those efforts.
It also points to specific ways to increase the chances that patients who are traditionally underrepresented in research can be included in the massive banks of genetic samples and data from digital medical records that underlie these efforts.
Not only could that be important to the accuracy of the tools based on those data, but it would also make it more likely that they’d benefit diverse patient communities.
The study, in the December issue of Health Affairs, comes from a team at the University of Michigan and Michigan State University that studied U-M’s efforts to build a large bank of data and samples for researchers to use.