UPhAIR: A Hybrid Pipeline for Generating Understandable Post-hoc AI Reports in Glioma IDH Mutation Status Prediction

Clinical adoption of machine learning (ML) in medical imaging is limited by the lack of interpretability. To address this, we present understandable post-hoc artificial intelligence reports (UPhAIR), a pipeline designed to generate transparent, evidence-based explanations by combining Shapley additive explanation (SHAP) analysis with retrieval-augmented generation (RAG) and large language models (LLMs). We trained 12 Classifiers to predict Isocitrate dehydrogenase (IDH) mutation status in glioma using radiomics and clinical features. SHAP values were used to identify key contributors to each prediction. Domain literature was collected from three sources and indexed within a RAG framework. Re