AI models are increasingly used in cardiovascular medicine, but there are concerns that they may not work equally well for people of different racial and ethnic backgrounds. This systematic review examined how accurate and clinically applicable AI models for diagnosing heart disease and predicting outcomes are across racial and ethnic groups.
Of 1,704 references screened, 11 studies were included, covering diagnosis, prognosis and the analysis of heart images. Nine of them (82%) found racial or ethnic bias, while two found no differences between groups. For example, an AI model predicting heart failure from electrocardiograms was less accurate for young Black patients than for young Asian or White patients, and AI tools analysing cardiac MRI scans were more accurate for White people than for people from minority ethnic groups.
The authors conclude that AI models can perform well for some groups and less accurately for others, and that bias should be addressed at every stage, from data collection to clinical use. They recommend methods to reduce bias, standardised ways of measuring fairness, considering social determinants of health rather than race alone, and greater transparency through the sharing of code and data.
Learn more about this review here: https://doi.org/10.1016/j.ejrad.2024.111867
Reference:
Cau, R., Pisu, F., Suri, J.S., & Saba, L. (2024). Addressing hidden risks: Systematic review of artificial intelligence biases across racial and ethnic groups in cardiovascular diseases. European Journal of Radiology, 111867.