Deep Learning for Alzheimer's: Progress, Pitfalls, and the Path to Clinical Readiness

The timely and accurate diagnosis of Alzheimer's disease is paramount for effective patient intervention and care. Recent technological leaps in machine learning (ML) and deep learning (DL) are revolutionizing this domain, applying sophisticated algorithms to neuroimaging, genetic, and clinical data. These methods aim to differentiate Alzheimer's patients from cognitively normal individuals and predict the progression of moderate cognitive impairment, offering a new frontier in early detection and disease management.

This systematic literature review scrutinizes studies published between 2019 and 2025, leveraging major datasets like the Alzheimer’s Disease Neuroimaging Initiative. It explores a spectrum of input data, from T1-weighted magnetic resonance imaging and electroencephalography to multimodal neuroimaging data. The methodologies span advanced techniques including voxel-wise three-dimensional convolutional neural networks, hybrid convolutional neural network–transformer architectures, and attention-based multimodal fusion frameworks, alongside established ML models such as Random Forest, Extreme Gradient Boosting, and Generalized Linear Models. Rigorous preprocessing—involving intensity correction, spatial normalization, skull stripping, and data augmentation through rotations, flips, and generative adversarial network–based oversampling—is crucial, and evaluation relies on primary metrics such as accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve. Increasingly, interpretability techniques such as Grad-CAM, Layer-Wise Relevance Propagation, and saliency maps are being adopted to visualize discriminative brain regions.

Despite the impressive progress, particularly with models integrating hybrid architectures and multimodal information which demonstrate enhanced robustness, the path to clinical readiness is fraught with challenges. The review identifies persistent issues such as class imbalance, subject-level data leakage, small dataset sizes, and notably, poor cross-cohort generalizability. External validation, a critical step for real-world application, remains limited.

For these advanced computational tools to truly benefit patients, future research should strategically emphasize key areas. As lead author Malik notes in the paper, "Future research should emphasize larger, multi-center datasets, standardized evaluation protocols, and interpretable models that are clinically meaningful and translatable." This emphasis on robust datasets, consistent evaluation, and transparent, practical models is crucial for bridging the gap between computational advancements and real-world clinical application.