Lack of trust in artificial intelligence (AI) models in medicine is still the key blockage in the use of AI as clinical decision support systems (CDSS). Although AI models are already performing excellently in medicine, their black-box nature entails that it is often impossible to understand why a particular decision was made. In the field of explainable AI (XAI), many good tools have already been developed to" explain" to a human expert, for example, which input features influenced a prediction. However, in the clinical domain, it is essential that these explanations lead to some degree of causal understanding by the domain expert in the context of a specific application domain. For this reason, we have developed an interactive XAI platform that allows the domain expert to ask counterfactual (" what-if") questions. Our platform allows a domain expert to observe how changes based on their questions affect the AI decision and the XAI explanation.