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 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.

Automated summary

AI systems in the field of medical imaging can exhibit unfair biases, and it is important to address the meaning of fairness and potential sources of biases, as well as implement strategies to mitigate them. An analysis of the current state of the field reveals strengths and areas for improvement, along with challenges and opportunities.