ReviewDementia and neurocognitive disorders2026
Diagnostic Accuracy of MRI-Based Artificial Intelligence Models for Distinguishing Alzheimer's Disease and Mild Cognitive Impairment From Cognitively Normal Controls: A Systematic Review and Meta-Analysis.
Review in Dementia and neurocognitive disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Magnetic resonance imaging (MRI)-based artificial intelligence (AI) models are increasingly applied to brain MRI for diagnosing Alzheimer's disease (AD) and mild cognitive impairment (MCI), but their overall diagnostic performance remains unclear. We systematically searched PubMed/MEDLINE, Embase, Web of Science, Scopus, and IEEE Xplore until February 15, 2026, for diagnostic accuracy studies of machine-learning or deep-learning models using structural brain MRI to distinguish AD vs. cognitively normal (CN) controls and MCI or late mild cognitive impairment (LMCI) vs. CN controls. Seven studies met inclusion criteria, contributing five AD vs. CN and two MCI/LMCI vs. CN tasks, predominantly using deep-learning architectures applied to Alzheimer's Disease Neuroimaging Initiative cohorts. For AD vs. CN (five studies), pooled sensitivity was 0.96 (95% confidence interval [CI], 0.93-0.97) and pooled specificity was 0.95 (95% CI, 0.92-0.97), indicating excellent discrimination. For MCI/LMCI vs. CN (two studies), sensitivity was consistently high (0.91-0.93), whereas specificity varied widely (0.54-0.98), limiting the interpretability of pooled estimates. MRI-based AI models therefore show strong performance for established AD but heterogeneous specificity for MCI, underscoring the need for larger, externally validated studies in diverse populations.
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