AI has transformed diabetes management, particularly through tools that predict glucose levels and support clinical decisions. However, there are concerns that these technologies may not work equally well for different racial, ethnic and socioeconomic groups. This systematic review assessed how fair AI-based glucose prediction models are, focusing on differences in performance between groups, how fairness is reported, and biases linked to physiological measures.
Of 1,243 articles initially identified, only 10 met the criteria for inclusion. Just one of them explicitly evaluated whether AI model performance differed by race. Reporting on fairness was rare: only 7% of AI diabetes studies documented ethnic or racial data, and almost none carried out fairness audits. The review also highlights known physiological differences: for the same average glucose level, African American patients tend to have higher HbA1c values than White patients. If HbA1c is used as a reference to train AI models, this difference can be built into them. Yet none of the AI studies reviewed took this into account.
The authors also note that traditional diabetes risk scores overestimate risk for White individuals and underestimate it for Black individuals, a bias that AI systems trained on these scores could inherit. They conclude that, without systematic efforts to reduce bias, AI-based diabetes technologies risk worsening existing health disparities, and call for future research to prioritise transparency, representativeness and fairness.
Learn more about this review here: https://doi.org/10.61186/ist.202502.05.04
Reference:
Alipour, M., & Alipour, A. (2025). Algorithmic Bias in AI-Based Diabetes Care: Systematic Review of Model Performance, Equity Reporting, and Physiological Label Bias. InfoScience Trends, 2(5), 33–46.