Toward precision neuroscience in Alzheimer's disease: the role of multimodal AI.

Background: Alzheimer's disease (AD) is increasingly understood as a biologically defined and heterogeneous continuum, requiring models that move beyond symptom-based diagnosis toward individualized risk prediction, stratification, and intervention. Objective: This mini-review outlines how precision neuroscience frameworks may support the characterization of AD by integrating multimodal biomarkers, systems biology, systems neurophysiology, digital health markers, and artificial intelligence (AI). Methods: We synthesize recent developments across multi-omics profiling, neuroimaging and electrophysiological biomarkers, AI-based speech analysis, and multimodal machine learning approaches, with emphasis on their potential contribution to biologically informed disease staging and personalized clinical decision-making. Results: Omics and systems biology approaches are expanding the characterization of molecular pathways involved in AD susceptibility, progression, and treatment response. Systems neurophysiology, including multimodal neuroimaging, electrophysiology, and whole-brain modeling, provides complementary markers of large-scale network disruption across the AD continuum. Digital health technologies, particularly AI-based speech analysis, offer scalable and ecologically valid tools for early risk enrichment and longitudinal monitoring. Multimodal AI models further enable the integration of heterogeneous clinical, molecular, imaging, genetic, and behavioral data into probabilistic representations of disease burden and progression. However, clinical translation remains constrained by interpretability, harmonization, validation, fairness, and accessibility challenges. Conclusion: Precision neuroscience offers a promising framework for reconceptualizing AD as a dynamically modeled and biologically stratified disorder. Future progress will depend on robust multimodal datasets, transparent AI methods, longitudinal validation, and equitable implementation strategies capable of supporting early detection, trial enrichment, and personalized prevention or treatment.