Science
Older adults with similar diagnoses can differ substantially in biology, function, behavior, environment, and response to intervention. The Stanford AITC develops shared AI and technology capabilities to support precision intervention development, testing, and implementation, helping investigators examine who may benefit, what intervention should be delivered, and when and how delivery should adapt.
Selected research directions
SU-AITC brings together complementary AI and technology approaches for precision interventions in aging and AD/ADRD.
These areas highlight ongoing and published work relevant to SU-AITC. Featured demos and projects will be added as the Center develops.
AI foundation models for learning scalable representations from multimodal brain and health data.
A multimodal foundation model framework integrating biological, psychological, environmental, and physical health data to learn comprehensive representations of the human brain and enable more precise diagnosis and personalized care.
AI-enabled tools for monitoring mobility, behavior, function, and health in aging and AD/ADRD.
A framework for using smartphones, wearables, and ambient sensing technologies to continuously monitor cognitive, motor, behavioral, and physiological health in neurodegenerative diseases.
Nerrise et al., Nature Reviews Bioengineering, 2026
Human-centered and adaptive interfaces designed to support personalized interventions and interaction with older adults.
A closed-loop framework in which multimodal sensing, AI-powered cognitive decoding, and adaptive actuators (neurofeedback, adaptive cognitive training tasks, and non-invasive brain stimulation) personalize cognitive enhancement for older adults.
Zhou et al., Ageing Research Reviews, 2025. See also pSOPT Online (Tapparello et al., IEEE/ACM CHASE, 2026).
Computational approaches for measuring and supporting engagement during technology-enabled interventions.
A framework that builds multi-dimensional engagement profiles from behavioral, self-report, physiological, passive sensing, and brain imaging measures, models them over time, and estimates engagement trajectories to guide intervention design.
Guimarães et al., Ageing Research Reviews, 2025
Funding
SU-AITC will support research teams advancing AI- and technology-enabled precision interventions for aging and AD/ADRD. Funding opportunities and supported projects will be announced here.