Skin of color (SoC), also known as ethnic skin, traditionally refers to the dark-skinned individuals of African, Asian, Native American, Middle Eastern, and Hispanic origins. As the clinical and dermoscopy images of skin lesions can differ from that encountered in the Caucasian populations, questions about arise how artificial intelligence (AI) models trained with large datasets of images mainly on light-skinned patients perform in case of an image with a cutaneous lesion on a dark-skinned individual. Poor quality images and deficiency of technology equipment as well as underrepresentation of skin of color in large datasets seem to make the implantation of AI to SoC dermatology research, diagnosis, and treatment even harder. It is worth mentioning that AI models have not yet been widely explored for diagnosing many dermatological conditions commonly seen in individuals with SoC. Inclusive research, ethical app development, strong regulatory oversight, and representation of SoC (both in datasets and among developers) must be prioritized. Only with such intentional and sustained efforts can AI in dermatology be shaped into an inclusive, globally relevant tool that improves care for all skin types.