  One principal impediment in the successful deployment of AI-based
Computer-Aided Diagnosis (CAD) systems in clinical workflows is their lack of
transparent decision making. Although commonly used eXplainable AI methods
provide some insight into opaque algorithms, such explanations are usually
convoluted and not readily comprehensible except by highly trained experts. The
explanation of decisions regarding the malignancy of skin lesions from
dermoscopic images demands particular clarity, as the underlying medical
problem definition is itself ambiguous. This work presents ExAID (Explainable
AI for Dermatology), a novel framework for biomedical image analysis, providing
multi-modal concept-based explanations consisting of easy-to-understand textual
explanations supplemented by visual maps justifying the predictions. ExAID
relies on Concept Activation Vectors to map human concepts to those learnt by
arbitrary Deep Learning models in latent space, and Concept Localization Maps
to highlight concepts in the input space. This identification of relevant
concepts is then used to construct fine-grained textual explanations
supplemented by concept-wise location information to provide comprehensive and
coherent multi-modal explanations. All information is comprehensively presented
in a diagnostic interface for use in clinical routines. An educational mode
provides dataset-level explanation statistics and tools for data and model
exploration to aid medical research and education. Through rigorous
quantitative and qualitative evaluation of ExAID, we show the utility of
multi-modal explanations for CAD-assisted scenarios even in case of wrong
predictions. We believe that ExAID will provide dermatologists an effective
screening tool that they both understand and trust. Moreover, it will be the
basis for similar applications in other biomedical imaging fields.
