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Addressing fairness in artificial intelligence for medical imaging

Author

Listed:
  • María Agustina Ricci Lara

    (Hospital Italiano de Buenos Aires
    Universidad Tecnológica Nacional)

  • Rodrigo Echeveste

    (Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET))

  • Enzo Ferrante

    (Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET))

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.

Suggested Citation

  • María Agustina Ricci Lara & Rodrigo Echeveste & Enzo Ferrante, 2022. "Addressing fairness in artificial intelligence for medical imaging," Nature Communications, Nature, vol. 13(1), pages 1-6, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32186-3
    DOI: 10.1038/s41467-022-32186-3
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    References listed on IDEAS

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    1. Frederick M. Howard & James Dolezal & Sara Kochanny & Jefree Schulte & Heather Chen & Lara Heij & Dezheng Huo & Rita Nanda & Olufunmilayo I. Olopade & Jakob N. Kather & Nicole Cipriani & Robert L. Gro, 2021. "The impact of site-specific digital histology signatures on deep learning model accuracy and bias," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
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