Clinical pathways matter for multimodal deep learning in early Alzheimer’s disease detection (opens in new tab)

Identifying individuals at risk of Alzheimer’s disease (AD), particularly in the preclinical and early stages, remains challenging. Although deep learning approaches based on structural MRI show promise as a non-invasive biomarker, existing multimodal models require task-specific training and depend on biomarkers that are not routinely available in clinical practice. Here, we propose a zero-shot multimodal feature extraction framework based on SigLIP that combines structural MRI embeddings wi...