AI systems now match expert performance in many medical imaging tasks, but there is growing concern that they may reflect and amplify human bias. This is especially worrying in the case of underdiagnosis, when AI wrongly labels a person who has a disease as healthy, which can delay their access to care. This study examined underdiagnosis by AI tools that analyse chest X-rays, using three large US datasets and a combination of all three, with more than 700,000 images from almost 130,000 patients.
The AI tools consistently and selectively underdiagnosed under-served groups: female patients, patients under 20, Black patients, Hispanic patients and patients with Medicaid, a public health insurance for people with low incomes. Underdiagnosis was even higher for patients who belonged to more than one of these groups, such as Hispanic female patients, and for young patients who were also female, Black or on Medicaid.
The authors warn that using AI tools with these biases could worsen existing inequalities in care and lead to unequal access to treatment, raising ethical concerns about using these models in clinical practice.
Learn more about this study here: https://doi.org/10.1038/s41591-021-01595-0
Reference:
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021). Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature Medicine, 27(12), 2176–2182.