Diabetes outcomes differ across ethnic and racial groups, yet AI tools for diabetes management are often developed without taking this into account. To assess how well these groups are represented, the authors re-analysed 141 articles from an earlier review of AI in diabetes care.
Only 10 of the 141 articles (7.1%) reported the ethnic or racial background of their participants. In those that reported race, participants were on average 69.5% White, 17.1% Black and 3.7% Asian, and only two articles included Native American participants. According to the authors, AI tools trained on such unrepresentative data risk producing biased results for the groups that are left out.
To address this, the authors propose a short questionnaire that researchers and clinicians can use to assess ethnoracial equity in AI diabetes research. It covers whether ethnoracial differences in the disease are discussed, whether the training data and its limitations are described, whether the training population reflects the population the tool will serve, and whether strategies to reduce bias are in place. As the authors put it, “AI can either mitigate or exacerbate disparities in health care.”
Learn more about this article here: https://doi.org/10.2196/22320
Reference:
Pham, Q., Gamble, A., Hearn, J., & Cafazzo, J.A. (2021). The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations. Journal of Medical Internet Research, 23(2), e22320.