CLARUS: An Interactive Explainable AI Platform for Manual Counterfactuals in Graph Neural Networks

BackgroundLack of trust in artificial intelligence (AI) models in medicine is still the key blockage for the use of AI in clinical decision support systems (CDSS). Although AI models are already performing excellently in systems medicine, their black-box nature entails that patient-specific decisions are incomprehensible for the physician. This is especially true for very complex models such as graph neural networks (GNNs), a common state-of-the-art approach to model biological networks such as protein-protein-interaction graphs (PPIs) to predict clinical outcomes. The aim of explainable AI (XAI) algorithms is to "explain" to a human domain expert, which input features, such as genes, influenced a specific recommendation. However, in the clinical domain, it is essential that these explanations lead to some degree of causal understanding by a clinician in the context of a specific application. ResultsWe developed the CLARUS platform, aiming to promote human understanding of GNN predictions by allowing the domain expert to validate and improve the decision-making process. CLARUS enables the visualisation of the patient-specific biological networks used to train and test the GNN model, where nodes and edges correspond to gene products and their interactions, for instance. XAI methods, such as GNNExplainer, compute relevance values for genes and interactions. The CLARUS graph visualisation highlights gene and interaction relevances by color intensity and line thickness, respectively. This enables domain experts to gain deeper insights into the biological network by identifying the most influential sub-graphs and molecular pathways crucial for the decision-making process. More importantly, the expert can interactively alter the patient-specific PPI network based on the acquired understanding and initiate re-prediction or retraining. This interactivity allows to ask manual counterfactual questions and analyse the resulting effects on the GNN prediction. ConclusionTo the best of our knowledge, we present the first interactive XAI platform prototype, CLARUS, that allows not only the evaluation of specific human counterfactual questions based on user-defined alterations of patient PPI networks and a re-prediction of the clinical outcome but also a retraining of the entire GNN after changing the underlying graph structures. The platform is currently hosted by the GWDG on https://rshiny.gwdg.de/apps/clarus/.