Optimal Transport (OT) is a powerful tool widely used in healthcare applications, but its high computational cost and sensitivity to data changes make it less practical for resource-constrained settings. These limitations also contribute to increased environmental impact due to higher CO2 emissions from computing. To address these challenges, we explore Unbalanced Optimal Transport (UOT), a variation of OT that is both computationally efficient and more robust to data variability. We apply UOT to two healthcare scenarios: independence testing on breast cancer data and modeling heart rate variability (HRV). Our experiments show that UOT not only reduces computational costs but also delivers reliable results, making it a practical alternative to OT for socially impactful applications.
