The literature cluster reveals two converging macro-trends in Alzheimer's disease (AD) research: the maturation of deep-learning diagnostic pipelines and the parallel construction of large-scale knowledge infrastructures (knowledge graphs, biobanks, Mendelian randomization frameworks) that support precision medicine, alongside a persistent concern about geographic and research-capacity inequities. On the technical side, convolutional neural network architectures—Xception, InceptionResNetV2, DenseNet201, ResNet50, MobileNetV2, InceptionV3, and LeNet-VGG—are being systematically enhanced with batch normalization, dropout, dense layers, and transfer learning, then rigorously validated via k-fold cross-validation on standardized datasets (Kaggle MRI Alzheimer's Data, with defined training/testing splits). These models are benchmarked with a consistent battery of metrics (accuracy, sensitivity, specificity, precision, recall, F1-score, ROC), with reported performance approaching or exceeding 99% accuracy, signaling a shift toward highly optimized, reproducible imaging-based diagnostic tools as an alternative or complement to costly biomarker assays.
A second major thread is the construction of computable knowledge resources that integrate heterogeneous biomedical data. The Alzheimer's Disease Knowledge Graph (ADKG), built from PubMed literature using NLP pipelines (SpERT model built on SciBERT, trained on the GPT-4-augmented ADERC corpus), exemplifies how relation-extraction AI is being used to systematize millions of entity mentions and triplets connecting genes, variants, drugs, and diseases. This knowledge graph, alongside resources like UK Biobank's proteomic/metabolomic cohorts, is positioned to support predictive modeling and validate causal hypotheses—linking directly to the Mendelian randomization studies that use genetic instruments and independent validation cohorts (accounting for horizontal pleiotropy via sensitivity analyses) to test causal relationships among telomere length, brain imaging phenotypes, and AD risk. Together these tools feed into the overarching goal of precision medicine, where genomic analysis, biomarker profiles, and imaging data jointly inform individualized diagnostic and therapeutic strategies, including hypothetical primary-prevention programs using amyloid-clearing antibodies stratified by APOE4 status and evaluated for lifetime cost-effectiveness against natural history outcomes.
Underlying these computational advances is a persistent equity concern, illustrated by the bibliometric analysis of Alzheimer's and dementia research in Latin America and the Caribbean. Despite representing 21 countries, the region contributes only 3% of global dementia publications, with Brazil alone producing nearly half of regional output—highlighting uneven cross-country collaboration and underinvestment in policy-focused dementia research. This regional disparity stands in contrast to the well-resourced, technologically intensive pipelines (SVM-based classification of demented status and treatment response, deep learning diagnostic models, large-scale knowledge graphs) emerging from high-resource settings, suggesting a widening gap between AI/precision-medicine innovation and equitable global research capacity that policy investment and cross-country collaboration are needed to close.