  This paper investigates the potential usage of large text-to-image (LTI)
models for the automated diagnosis of a few skin conditions with rarity or a
serious lack of annotated datasets. As the input to the LTI model, we provide
the targeted instantiation of a generic but succinct prompt structure designed
upon careful observations of the conditional narratives from the standard
medical textbooks. In this regard, we pave the path to utilizing accessible
textbook descriptions for automated diagnosis of conditions with data scarcity
through the lens of LTI models. Experiments show the efficacy of the proposed
framework, including much better localization of the infected regions.
Moreover, it has the immense possibility for generalization across the medical
sub-domains, not only to mitigate the data scarcity issue but also to debias
automated diagnostics from the all-pervasive racial biases.
