Cardiovascular disease is the leading cause of death worldwide, but its prevalence, burden and outcomes vary across gender and racial groups. Some biomarkers also vary between populations and ethnic and racial groups. Inequalities in who is included in clinical trials, how the disease presents, and how it is diagnosed and treated are preserved in the health data later used to train AI, which can lead to biased model performance.
This review is intended as a guide for AI development teams working in cardiovascular imaging. It describes the implicit and explicit biases in cardiovascular care that may be present in existing datasets without being obvious to developers, and explains how assumptions in the data and in the outcomes chosen to train and evaluate a model can affect how it performs in the real world.
The authors review disparities in cardiovascular outcomes across gender and racial groups, differences in the treatment of historically marginalised groups, and gaps in clinical trial representation. They also summarise research showing bias in cardiovascular AI, and highlight that AI can also help reduce bias, for example through tools that audit models for bias.
Learn more about this review here: https://doi.org/10.1007/s11883-024-01190-x
Reference:
van Assen, M., Beecy, A., Gershon, G., Newsome, J., Trivedi, H., & Gichoya, J. (2024). Implications of Bias in Artificial Intelligence: Considerations for Cardiovascular Imaging. Current Atherosclerosis Reports, 26, 91–102