Addressing fairness in artificial intelligence for medical imaging

Artículo

Autoría:

Ricci Lara, María Agustina ; ECHEVESTE, RODRIGO SEBASTIÁN ; FERRANTE, ENZO

Fecha:

2022

Editorial y Lugar de Edición:

Springer Nature

Revista:

NATURE COMMUNICATIONS, vol. 13 (pp. 1-6) Springer Nature

Resumen

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.

Palabras Clave

FairnessMedical ImagingArtificial Intelligence

Descargue o solicite el texto completo:

http://hdl.handle.net/11336/213281