  Dermatological classification algorithms developed without sufficiently
diverse training data may generalize poorly across populations. While
intentional data collection and annotation offer the best means for improving
representation, new computational approaches for generating training data may
also aid in mitigating the effects of sampling bias. In this paper, we show
that DALL$\cdot$E 2, a large-scale text-to-image diffusion model, can produce
photorealistic images of skin disease across skin types. Using the Fitzpatrick
17k dataset as a benchmark, we demonstrate that augmenting training data with
DALL$\cdot$E 2-generated synthetic images improves classification of skin
disease overall and especially for underrepresented groups.
