AI-Based Differential Diagnosis of Neurodegenerative Diseases Using Structural MRI: A Systematic Review
Abstract
Accurate differential diagnosis among neurodegenerative diseases is a major clinical challenge due to overlapping symptomatology and pathologies. Artificial intelligence (AI) applied to structural MRI (sMRI) offers a promising, noninvasive approach for identifying subtle, disease-specific patterns of brain atrophy. This systematic review evaluates the landscape of AI methods for this purpose, focusing on the differential diagnosis between neurodegenerative diseases while excluding isolated disease-versus-control comparisons. Following the PRISMA guidelines, we analyzed 82 studies published between January 2008 and December 2025. Our analysis reveals a distinct methodological evolution from classical machine learning (ML), which relies on handcrafted features, to end-to-end deep learning (DL) architectures that automatically learn representations directly from image data. While models often report high classification accuracy (typically exceeding 90% for binary tasks), accuracy ranges from 70% to 92% for standard 3-class tasks, and drops to 65% to 88% for broader multi-disease frameworks (≥ 4 classes) or out-of-domain evaluations. Furthermore, we identify systemic barriers that delay clinical translation: a widespread reliance on private datasets (77% of studies), a severe lack of rigorous out-of-domain validation (only 28% of studies), a low rate of public code availability (20% of studies), and limited use of pathologically or biologically confirmed diagnostic labels (only 18% of studies). These challenges in data infrastructure and validation standards undermine model generalizability and cast doubt on the clinical relevance of …