  Would you trust physicians if they cannot explain their decisions to you?
Medical diagnostics using machine learning gained enormously in importance
within the last decade. However, without further enhancements many
state-of-the-art machine learning methods are not suitable for medical
application. The most important reasons are insufficient data set quality and
the black-box behavior of machine learning algorithms such as Deep Learning
models. Consequently, end-users cannot correct the model's decisions and the
corresponding explanations. The latter is crucial for the trustworthiness of
machine learning in the medical domain. The research field explainable
interactive machine learning searches for methods that address both
shortcomings. This paper extends the explainable and interactive CAIPI
algorithm and provides an interface to simplify human-in-the-loop approaches
for image classification. The interface enables the end-user (1) to investigate
and (2) to correct the model's prediction and explanation, and (3) to influence
the data set quality. After CAIPI optimization with only a single
counterexample per iteration, the model achieves an accuracy of $97.48\%$ on
the Medical MNIST and $95.02\%$ on the Fashion MNIST. This accuracy is
approximately equal to state-of-the-art Deep Learning optimization procedures.
Besides, CAIPI reduces the labeling effort by approximately $80\%$.
