AI health algorithms are increasingly used to predict risk and support treatment decisions, often outperforming humans in diagnostic accuracy. However, they can also underestimate risk for marginalised and underserved patients, delaying the treatment they need. This scoping review analysed 23 sources published between 2020 and 2024 to examine racial and ethnic bias in these algorithms, the role of oversight and stakeholders, and how this bias can be reduced.
A common explanation for this bias is that algorithms are trained on data in which majority populations are overrepresented, making them less reliable for others. Black, Native American, Asian and Latinx populations are often underrepresented in health datasets. Bias can also come from people: racial stereotypes absorbed during medical training can affect how clinicians assess patients, and these assessments end up in the health records used to train algorithms. The authors also describe a feedback loop: when patients from marginalised groups distrust the health system and use it less, data about them becomes even less representative, and the algorithms less accurate for them.
To address this, the review highlights the need for continuous monitoring of algorithms once they are in use, in a similar way to how medicines are monitored after approval, and for retraining or withdrawing those that create inequities. It also stresses that simply including or removing race from an algorithm does not solve the problem, and that responsibility should be shared by developers, health systems, clinicians, regulators and patients. The authors conclude that, without this oversight, AI in healthcare risks entrenching health inequities.
Learn more about this review here: https://doi.org/10.1080/13557858.2024.2422848
Reference:
Hussain, S.A., Bresnahan, M., & Zhuang, J. (2025). The bias algorithm: how AI in healthcare exacerbates ethnic and racial disparities – a scoping review. Ethnicity & Health, 30(2), 197–214.