A Hierarchical Explainable AI Framework for Anemia Diagnosis: A Conceptual Model Targeting Rare Diseases

This preprint presents a conceptual hierarchical explainable AI (XAI) framework for anemia diagnosis, with a particular focus on the early detection of rare hematological disorders. The proposed Clinical Decision Support System (CDSS) integrates CBC-based triage, specialized anemia classification pathways, anomaly detection, few-shot learning, and interpretable reporting mechanisms designed to simulate expert stepwise clinical reasoning. The framework aims to support transparent differential diagnosis generation and improve recognition of atypical laboratory patterns associated with rare diseases. This work represents a conceptual research proposal and has not yet undergone clinical implementation, prospective validation, or peer review.