Algorithmic individual fairness and healthcare: a scoping review

The use of algorithms in healthcare holds the potential to improve care delivery and reduce costs. However, these algorithms can sometimes reflect biases, leading to unfair treatment of individuals, particularly those from marginalized groups. This study reviews the concept of algorithmic individual fairness (IF), which ensures that similar individuals are treated similarly. The review identifies various philosophies and methods used to achieve IF and highlights how they can address biases in healthcare. While IF approaches are still in their early stages, they show promise in reducing disparities in healthcare. The findings emphasize the need for further research to enhance fairness in healthcare algorithms and ensure equitable treatment for individuals.