Foundation models are reshaping artificial intelligence and are beginning to transform healthcare and brain health research. Unlike traditional AI systems that primarily focus on individual modalities or narrowly defined tasks, next-generation foundation models for brain health aim to integrate heterogeneous and multimodal data sources to enable population-scale assessment and personalized monitoring of cognitive health.
This talk provides an overview of emerging trends in foundation models for brain health and discusses several key challenges, including learning from heterogeneous and largely unlabeled medical data, ensuring trustworthy multimodal reasoning, and identifying scalable digital signals for the early detection of cognitive decline. Illustrative examples from recent work on neuroimaging foundation models, medical multimodal large language models, and digital phenotyping will be presented to highlight both the opportunities and limitations of current approaches. The talk concludes with a perspective on how trustworthy multimodal foundation models may enable continuous, preventive, and personalized brain healthcare at population scale.
