AI is increasingly being used to predict a person’s risk of developing type 2 diabetes, which could help delay or prevent the disease through earlier, more personalised prevention. This editorial discusses a scoping review of 40 studies on AI models for diabetes risk prediction. It found that models combining electronic health records with other types of data, such as genetic or medical imaging data, performed better than models using a single type of data.
However, the authors warn that these results should be approached with caution. Many of the models showed a pronounced imbalance in gender, ethnicity and age in the data used to train them, only five were tested on external data, and most were not evaluated across different demographic groups. Since Black and Mexican Americans have a higher prevalence of diabetes than White Americans, leaving certain groups out of training data could worsen health inequities for populations already at greater risk.
To address this, the authors call for mandatory representation criteria for the approval and adoption of these models, tools to assess their risk of bias, and external validation. They argue that AI cannot be adopted on a large scale in healthcare until problems with data quality and bias are addressed, and that this requires collaboration between clinicians, researchers, policymakers and entrepreneurs.
Learn more about this article here: https://doi.org/10.1038/s41746-024-01034-7
Reference:
Wang, S. C. Y., Nickel, G., Venkatesh, K. P., Raza, M. M., & Kvedar, J. C. (2024). AI-based diabetes care: Risk prediction models and implementation concerns. npj Digital Medicine, 7, 36.
