Addressing fairness in artificial intelligence for medical imaging

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...

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Autores principales: Ricci Lara, María Agustina, Echeveste, Rodrigo, Ferrante, Enzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Comment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357063/
https://www.ncbi.nlm.nih.gov/pubmed/35933408
http://dx.doi.org/10.1038/s41467-022-32186-3
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author Ricci Lara, María Agustina
Echeveste, Rodrigo
Ferrante, Enzo
author_facet Ricci Lara, María Agustina
Echeveste, Rodrigo
Ferrante, Enzo
author_sort Ricci Lara, María Agustina
collection PubMed
description 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.
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spelling pubmed-93570632022-08-08 Addressing fairness in artificial intelligence for medical imaging Ricci Lara, María Agustina Echeveste, Rodrigo Ferrante, Enzo Nat Commun Comment 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. Nature Publishing Group UK 2022-08-06 /pmc/articles/PMC9357063/ /pubmed/35933408 http://dx.doi.org/10.1038/s41467-022-32186-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Comment
Ricci Lara, María Agustina
Echeveste, Rodrigo
Ferrante, Enzo
Addressing fairness in artificial intelligence for medical imaging
title Addressing fairness in artificial intelligence for medical imaging
title_full Addressing fairness in artificial intelligence for medical imaging
title_fullStr Addressing fairness in artificial intelligence for medical imaging
title_full_unstemmed Addressing fairness in artificial intelligence for medical imaging
title_short Addressing fairness in artificial intelligence for medical imaging
title_sort addressing fairness in artificial intelligence for medical imaging
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357063/
https://www.ncbi.nlm.nih.gov/pubmed/35933408
http://dx.doi.org/10.1038/s41467-022-32186-3
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