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. Relevant papers were retrieved using Facebook AI similarity search (FAISS) vector similarity search and provided to Google Gemini 2.5 Pro to generate concise, reference-supported explanations for each feature. The model achieved a best AUC of 0.90{+/-}0.02 on a 5-fold cross-validation using an extreme gradient boosting (XGBoost) Classifier and a hold-out test AUC of 0.86. In a case study of a single patient excluded from training, the model correctly predicted the patient to be IDH-wildtype glioma, and SHAP identified MGMT status, age, and three radiomic features as the most influential features. UPhAIR produced a structured report combining SHAP visualizations with LLM-generated summaries grounded in scientific evidence. UPhAIR provides a practical, model-agnostic framework that enhances ML interpretability in clinical settings, helping bridge the gap between black-box AI and real-world medical decision-making.