AI offers opportunities to transform cardiovascular medicine, but AI algorithms can also have biases of their own. In this commentary, the authors describe several sources of bias in cardiovascular AI. These include differences in how diagnostic tests such as echocardiograms and cardiac MRI are carried out and interpreted, models that learn patterns from their training data that do not hold in new populations, and the biases that clinicians may bring, consciously or not, when labelling the data used to train them.
A particular concern is that AI could worsen health disparities. Women and minority groups have historically been under-represented in cardiology research, so much of the current evidence may not apply to them. The authors give examples of inequalities that end up in the data used to train AI: men are 2.5 times more likely than women to be referred to a cardiologist for chest pain, and Black patients in emergency departments are 40% less likely to receive pain medication than White patients. In another example, an algorithm identified patients’ postcodes, linked to low-income and predominantly Black neighbourhoods, as the main predictor of a longer hospital stay.
To prevent this, the authors call for clinicians, ethicists and representatives of minority groups to be involved from the early stages of AI development, for algorithms to be validated in minority and low-income groups, and for regulatory and ethical guidelines to ensure that the benefits of AI are shared equally, along with regular monitoring of AI models once they are in use.
Learn more about this article here: https://doi.org/10.1016/S2589-7500(20)30249-1
Reference:
Tat, E., Bhatt, D.L., & Rabbat, M.G. (2020). Addressing bias: artificial intelligence in cardiovascular medicine. The Lancet Digital Health, 2(12), e635–e636.