  Equity in AI for healthcare is crucial due to its direct impact on human
well-being. Despite advancements in 2D medical imaging fairness, the fairness
of 3D models remains underexplored, hindered by the small sizes of 3D fairness
datasets. Since 3D imaging surpasses 2D imaging in SOTA clinical care, it is
critical to understand the fairness of these 3D models. To address this
research gap, we conduct the first comprehensive study on the fairness of 3D
medical imaging models across multiple protected attributes. Our investigation
spans both 2D and 3D models and evaluates fairness across five architectures on
three common eye diseases, revealing significant biases across race, gender,
and ethnicity. To alleviate these biases, we propose a novel fair identity
scaling (FIS) method that improves both overall performance and fairness,
outperforming various SOTA fairness methods. Moreover, we release
Harvard-FairVision, the first large-scale medical fairness dataset with 30,000
subjects featuring both 2D and 3D imaging data and six demographic identity
attributes. Harvard-FairVision provides labels for three major eye disorders
affecting about 380 million people worldwide, serving as a valuable resource
for both 2D and 3D fairness learning. Our code and dataset are publicly
accessible at
\url{https://ophai.hms.harvard.edu/datasets/harvard-fairvision30k}.
