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Editorial
. 2024 Jul 4;47(2):e1077.
doi: 10.23938/ASSN.1077.

[Health for whom? Intersectionality and biases in the use of artificial intelligence in clinical diagnosis]

[Article in Spanish]
Sua Amaya-Santos  1 , Jaime Jiménez-Pernett  2 , Clara Bermudez-Tamayo  3
Affiliations
Editorial

[Health for whom? Intersectionality and biases in the use of artificial intelligence in clinical diagnosis]

[Article in Spanish]
Sua Amaya-Santos et al. An Sist Sanit Navar. .
No abstract available

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Figures

Figura 1
Figura 1. Estudio de sesgos para su mitigación con enfoque transdisciplinar
Figura 2
Figura 2. Sesgo de interseccionalidad en el diagnóstico apoyado por Inteligencia Artificial.
Figura 3
Figura 3. Proceso para una Inteligencia Artificial responsable en salud. Elaboración propia a partir de Sujan y col 2023.
See this image and copyright information in PMC

References

    1. Bermudez-Tamayo C, Jimenez-Pernett J. Inteligencia artificial para el avance de los sistemas de salud. Posibles aportes y retos. Revista de Derecho de la Seguridad Social Laborum. 2022;4(especial):401–414. https://revista.laborum.es/index.php/revsegsoc/article/view/641/735
    1. Peiró S. El futuro de la salud pública tras la pandemia. Una ventana de oportunidad. An Sist Sanit Navar. 2023;46(2):e1045. doi: 10.23938/ASSN.1045. - DOI - PMC - PubMed
    1. Murphy K, Di Ruggiero E, Upshur R, Willison D, Malhotra R, Cai J. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. 2021;22(1):14–14. doi: 10.1186/s12910-021-00577-8. - DOI - PMC - PubMed
    1. Miller D, Brown E. Artificial intelligence in medical practice: The question to the answer? Am J Med. 2018;131(2):129–133. doi: 10.1016/j.amjmed.2017.10.035. - DOI - PubMed
    1. Cirillo D, Catuara-Solarz S, Morey C, Guney E, Subirats L, Mellino S, et al. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. NPJ Digit Med. 2020;3:81–81. doi: 10.1038/s41746-020-0288-5. - DOI - PMC - PubMed

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