  A lot of deep learning (DL) research these days is mainly focused on
improving quantitative metrics regardless of other factors. In human-centered
applications, like skin lesion classification in dermatology, DL-driven
clinical decision support systems are still in their infancy due to the limited
transparency of their decision-making process. Moreover, the lack of procedures
that can explain the behavior of trained DL algorithms leads to almost no trust
from clinical physicians. To diagnose skin lesions, dermatologists rely on
visual assessment of the disease and the data gathered from the patient's
anamnesis. Data-driven algorithms dealing with multi-modal data are limited by
the separation of feature-level and decision-level fusion procedures required
by convolutional architectures. To address this issue, we enable single-stage
multi-modal data fusion via the attention mechanism of transformer-based
architectures to aid in diagnosing skin diseases. Our method beats other
state-of-the-art single- and multi-modal DL architectures in image-rich and
patient-data-rich environments. Additionally, the choice of the architecture
enables native interpretability support for the classification task both in the
image and metadata domain with no additional modifications necessary.
