  In the current development and deployment of many artificial intelligence
(AI) systems in healthcare, algorithm fairness is a challenging problem in
delivering equitable care. Recent evaluation of AI models stratified across
race sub-populations have revealed inequalities in how patients are diagnosed,
given treatments, and billed for healthcare costs. In this perspective article,
we summarize the intersectional field of fairness in machine learning through
the context of current issues in healthcare, outline how algorithmic biases
(e.g. - image acquisition, genetic variation, intra-observer labeling
variability) arise in current clinical workflows and their resulting healthcare
disparities. Lastly, we also review emerging technology for mitigating bias via
federated learning, disentanglement, and model explainability, and their role
in AI-SaMD development.
