Artificial Intelligence for Magnetic Resonance Imaging-Based Diagnosis of Alzheimer's Disease
TL;DR
Findings varied across datasets, tasks, validation procedures, and populations, while high accuracies were often derived from curated datasets and internal validation, which restrict clinical generalizability.
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder requiring timely diagnosis. This structured narrative review examines artificial intelligence (AI) and deep-learning applications (DL) for MRI-based diagnosis of AD and mild cognitive impairment (MCI). PubMed and Web of Science were searched between April-June 2026; Scopus and ScienceDirect supported supplementary discovery. Studies were purposively selected using predefined eligibility criteria, with 42 primary studies summarized. Convolutional neural networks, transformers, transfer learning, and multimodal approaches frequently reported promising classification performance. However, findings varied across datasets, tasks, validation procedures, and populations, while high accuracies were often derived from curated datasets and internal validation. Limited external and prospective validation, potential over fitting or data leakage, class imbalance, and insufficient demographic and scanner diversity restrict clinical generalizability. Independent multicenter and real-world assessments are required prior to routine clinical adoption.