Digital health technologies produce large amounts of data that can be used to train AI, which is transforming cardiovascular care. However, AI can become biased when the data used to train it comes from a very similar group of people, or from healthcare processes that are themselves unequal. Bias can also be introduced while algorithms are developed, tested and put into practice, and even after they are already in use.
Biased algorithms can lead to missed diagnoses, misclassified disease, inaccurate risk predictions and inappropriate treatment recommendations, and these harms can fall disproportionately on marginalised groups. This paper, part of a series in The Lancet Digital Health, gives an overview of how AI is used in cardiovascular care, explains where bias can arise at each stage of an algorithm’s development, and presents examples of harm caused by biased algorithms.
The authors also propose strategies to reduce bias during the training, testing and implementation of algorithms, so that “all those at risk for or living with cardiovascular disease might benefit equally from AI.”
Learn more about this article here: https://doi.org/10.1016/S2589-7500(24)00155-9
Reference:
Mihan, A., Pandey, A., & Van Spall, H. G. C. (2024). Mitigating the risk of artificial intelligence bias in cardiovascular care. The Lancet Digital Health, 6(10), e749–e754.