Addressing fairness in artificial intelligence for medical imaging

María Agustina Ricci Lara (), Rodrigo Echeveste () and Enzo Ferrante ()
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María Agustina Ricci Lara: Hospital Italiano de Buenos Aires
Rodrigo Echeveste: Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET)
Enzo Ferrante: Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET)

Nature Communications, 2022, vol. 13, issue 1, 1-6

Abstract: A plethora of work has shown that AI systems can systematically and unfairly be biased against certain populations in multiple scenarios. The field of medical imaging, where AI systems are beginning to be increasingly adopted, is no exception. Here we discuss the meaning of fairness in this area and comment on the potential sources of biases, as well as the strategies available to mitigate them. Finally, we analyze the current state of the field, identifying strengths and highlighting areas of vacancy, challenges and opportunities that lie ahead.

Date: 2022
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DOI: 10.1038/s41467-022-32186-3

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