The data management crisis behind AI-based brain MRI diagnosis:Heterogeneity, governance, and reproducibility

Abstract

AI-based brain magnetic resonance imaging (MRI) classifiers face a persistent gap between laboratory performance and clinical deployability. We argue the bottleneck is not model architecture but the absence of principled data management infrastructure. Drawing on our ongoing work in explainable multi-disease MRI classification, we identify five critical data management challenges: heterogeneity, governance misalignment, pipeline irreproducibility, annotation provenance, and demographic bias. To address these issues, we propose a minimal proof-of-concept metadata harmonization toolkit as a practical and tractable starting point for standardization. We further outline a validation protocol that measures field-level mapping coverage, clinician-reviewed preservation of phenotype and severity-score semantics, provenance completeness, and downstream classifier transfer across ADNI and OASIS-3. We invite the data management community to co-design the schema-mapping and query-optimization layers.