Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

Abstract Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (...

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Autores principales:	Davide Cirillo, Silvina Catuara-Solarz, Czuee Morey, Emre Guney, Laia Subirats, Simona Mellino, Annalisa Gigante, Alfonso Valencia, María José Rementeria, Antonella Santuccione Chadha, Nikolaos Mavridis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/a828eca8fdeb4d18a319bb90784a8994
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spelling	oai:doaj.org-article:a828eca8fdeb4d18a319bb90784a89942021-12-02T15:02:32ZSex and gender differences and biases in artificial intelligence for biomedicine and healthcare10.1038/s41746-020-0288-52398-6352https://doaj.org/article/a828eca8fdeb4d18a319bb90784a89942020-06-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0288-5https://doaj.org/toc/2398-6352Abstract Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.Davide CirilloSilvina Catuara-SolarzCzuee MoreyEmre GuneyLaia SubiratsSimona MellinoAnnalisa GiganteAlfonso ValenciaMaría José RementeriaAntonella Santuccione ChadhaNikolaos MavridisNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Davide Cirillo Silvina Catuara-Solarz Czuee Morey Emre Guney Laia Subirats Simona Mellino Annalisa Gigante Alfonso Valencia María José Rementeria Antonella Santuccione Chadha Nikolaos Mavridis Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
description	Abstract Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.
format	article
author	Davide Cirillo Silvina Catuara-Solarz Czuee Morey Emre Guney Laia Subirats Simona Mellino Annalisa Gigante Alfonso Valencia María José Rementeria Antonella Santuccione Chadha Nikolaos Mavridis
author_facet	Davide Cirillo Silvina Catuara-Solarz Czuee Morey Emre Guney Laia Subirats Simona Mellino Annalisa Gigante Alfonso Valencia María José Rementeria Antonella Santuccione Chadha Nikolaos Mavridis
author_sort	Davide Cirillo
title	Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
title_short	Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
title_full	Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
title_fullStr	Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
title_full_unstemmed	Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
title_sort	sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/a828eca8fdeb4d18a319bb90784a8994
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Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

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