Beyond Known Reality: Exploiting Counterfactual Explanations for Medical Research

This study employs counterfactual explanations to explore "what if?" scenarios in medical research, with the aim of expanding our understanding beyond existing boundaries. Specifically, we focus on utilizing MRI features for diagnosing pediatric posterior fossa brain tumors as a case study. The field of artificial intelligence and explainability has witnessed a growing number of studies and increasing scholarly interest. However, the lack of human-friendly interpretations in explaining the outcomes of machine learning algorithms has significantly hindered the acceptance of these methods by clinicians in their clinical practice. To address this, our approach incorporates counterfactual explanations, providing a novel way to examine alternative decision-making scenarios. These explanations offer personalized and context-specific insights, enabling the validation of predictions and clarification of variations under diverse circumstances. Importantly, our approach maintains both statistical and clinical fidelity, allowing for the examination of distinct tumor features through alternative realities. Additionally, we explore the potential use of counterfactuals for data augmentation and evaluate their feasibility as an alternative approach in medical research. The results demonstrate the promising potential of counterfactual explanations to enhance trust and acceptance of AI-driven methods in clinical settings.