A Hierarchical Explainable AI Framework for Anemia Diagnosis: A Conceptual Model Targeting Rare Diseases
Year: 2026
Type: preprint
Abstract: 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.
Institution: Tehran University of Medical Sciences
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7162695684
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