AI can help identify people at risk of cardiovascular disease, detect it early and allow timely intervention. However, bias can arise at any step in the development, validation and use of these algorithms, causing them to perform poorly for historically marginalised groups. This article discusses the sources and consequences of AI bias in predicting and detecting cardiovascular disease, and presents strategies to reduce it.
Bias can come from training data that does not represent the population a tool is meant for, from measurements or devices that work less well for some groups, and from leaving out important factors such as social determinants of health. It can also reflect inequalities in care: because women and ethnic minority patients are more likely to have their cardiovascular disease missed or diagnosed late, algorithms trained on this data may be less accurate at detecting it in these groups. Unequal access to digital technologies can further limit how well some groups are represented in the data. The authors give several examples of AI models that performed worse for women, Black patients or older adults.
To reduce bias, the authors propose an AI health equity framework covering the whole lifecycle of an algorithm: diverse research teams, early involvement of patients, representative and balanced data, the inclusion of social determinants of health, input from clinicians, validation across different groups, transparent reporting of results and monitoring after implementation. They conclude that, without these measures, AI bias could worsen existing healthcare inequities and miss opportunities to predict and detect cardiovascular disease.
Learn more about this article here: https://doi.org/10.1038/s44325-024-00031-9
Reference:
Mihan, A., Pandey, A., & Van Spall, H.G.C. (2024). Artificial intelligence bias in the prediction and detection of cardiovascular disease. npj Cardiovascular Health, 1, 31.