Artificial Intelligence-integrated Multimodal Neural Biomarkers for Early Risk Stratification in Preclinical Alzheimer's Disease.

INTRODUCTION Alzheimer's Disease (AD) develops biologically years before overt cognitive symptoms, creating a need for preclinical risk stratification beyond single biomarker tests.

Methods

This structured narrative review searched PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar for English-language literature published from 2009 to July 2026 on preclinical AD, multimodal biomarkers, and AI-based risk prediction. Evidence was synthesized qualitatively; PRISMA was not applied because the review was not systematic.

Results

Individual biomarkers provide complementary but incomplete signals and are limited by biological discordance, temporal heterogeneity, mixed pathologies, cost, invasiveness, and variable clinical availability. Machine learning, deep learning, graph neural networks, and transformer- based models can integrate heterogeneous data, model longitudinal trajectories, and estimate individual risk of amyloid or tau positivity, cognitive decline, and clinical conversion.

Discussion

Clinical translation requires longitudinal and externally validated datasets, standardized assays and imaging pipelines, population-specific calibration, transparent reporting, management of missing data and bias, privacy protection, and workable clinical integration.

Conclusion

Multimodal AI can shift AD assessment from late symptom-based diagnosis toward scalable, biologically informed, patient-specific preclinical risk prediction, supporting biomarker triage, trial enrichment, and future clinical decision-making.