Addressing fairness in artificial intelligence for medical imaging.

Journal: Nature communications
Published Date:

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.

Authors

  • María Agustina Ricci Lara
    Health Informatics Department, Hospital Italiano de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina; Universidad Tecnológica Nacional, Facultad Regional Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina.
  • Rodrigo Echeveste
    Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK. recheveste@sinc.unl.edu.ar.
  • Enzo Ferrante