AIM 1: In Uganda and Kenya, Kaposi Sarcoma (KS) is one of the most common cancers and a leading cause of cancer-related deaths in men. However, tracking how well patients are responding to treatment is challenging, especially in busy clinics. Doctors must measure the size and changes in the areas affected by the cancer by hand, which can take a lot of time and can vary depending on the doctor. This method does not work well for patients with dark skin, which can lead to biases in treatment compared to patients with fairer skin. To solve this problem, the investigators suggest the use of a simple, low-cost device that uses Artificial Intelligence (AI), the SkinScan3D (SS3D) device, to measure the cancer spots accurately and monitor changes over time. The goal of this study is to improve the SkinScan3D (SS3D) tool and the how it is used. The investigators will do this by talking to healthcare workers and patients, giving them surveys, and holding design workshops. The feedback from these activities will be shared with the device maker and local research teams to make the device better and ensure it is used properly. AIMS 2 \& 3: In this study the investigators seek to formally compare reproducibility and accuracy of KS lesion size measurements between SkinScan3D and the current standard of care manual measurement method. The investigators will then test the SS3D device on 100 patients in a variety of real-world practice locations to determine whether the device will be usable, acceptable, appropriate, and feasible in routine care settings.