  A shortage of dermatologists causes long wait times for patients who seek
dermatologic care. In addition, the diagnostic accuracy of general
practitioners has been reported to be lower than the accuracy of artificial
intelligence software. This article describes the Skincare project (H2020, EIT
Digital). Contributions include enabling technology for clinical decision
support based on interactive machine learning (IML), a reference architecture
towards a Digital European Healthcare Infrastructure (also cf. EIT MCPS),
technical components for aggregating digitised patient information, and the
integration of decision support technology into clinical test-bed environments.
However, the main contribution is a diagnostic and decision support system in
dermatology for patients and doctors, an interactive deep learning system for
differential diagnosis of malignant skin lesions. In this article, we describe
its functionalities and the user interfaces to facilitate machine learning from
human input. The baseline deep learning system, which delivers state-of-the-art
results and the potential to augment general practitioners and even
dermatologists, was developed and validated using de-identified cases from a
dermatology image data base (ISIC), which has about 20000 cases for development
and validation, provided by board-certified dermatologists defining the
reference standard for every case. ISIC allows for differential diagnosis, a
ranked list of eight diagnoses, that is used to plan treatments in the common
setting of diagnostic ambiguity. We give an overall description of the outcome
of the Skincare project, and we focus on the steps to support communication and
coordination between humans and machine in IML. This is an integral part of the
development of future cognitive assistants in the medical domain, and we
describe the necessary intelligent user interfaces.
