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Fairness metrics for health AI: we have a long way to go by Amarachi B. Mbakwe; Ismini Lourentzou; Leo Anthony Celi; Joy T. Wu is a Medicine article available to read on EtoBox.

What is Fairness metrics for health AI: we have a long way to go about?

The use of Artificial Intelligence (AI) is on track to revolutionize healthcare, with performance in medical tasks such as clinical diagnosis often being comparable to expert-level accuracy, at least in the laboratory. AI can play a significant role in healthcare, enabling clinicians to make more accurate and timely diagnoses and devise effective treatment plans. However, the amplification of pre-existing healthcare inequity with the use of AI models is a legitimate concern. Recent works have shown that medical imaging AI models can easily encode and learn patient-sensitive characteristics 1 and cause significant performance disparities between patient subgroups. 2 Therefore, it is encouraging to see more attempts, such as from Glocker et al., 3 that evaluate methods for assessing how sensitive patient information, such as ethnicity and sex, are encoded and possibly used in model predictions. Unfortunately, for many diagnostic and prognostic clinical applications, the "ground truth" used for fairness assessment metrics may already be embedded with biases and laced with suboptimal outcomes that are not explained by clinical features.

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Author

Amarachi B. Mbakwe; Ismini Lourentzou; Leo Anthony Celi; Joy T. Wu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Life Sciences)