ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025
A vision-language foundation model for Alzheimer's disease diagnosis using MRI and clinical data.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Adaptation of Language Models for Clinical Decision-Making in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- A vision-language foundation model for Alzheimer's disease diagnosis using MRI and clinical data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
introductionReliable early detection of Alzheimer's disease (AD) remains difficult due to heterogeneous progression trajectories and variability in clinical presentation. Multimodal approaches leveraging neuroimaging and clinical data offer promise but often struggle with effective integration and generalization.
methodsWe developed Alzheimer's Disease Language and Image Pre-Training (ADLIP), a vision-language framework that integrates 3D T1-weighted magnetic resonance imaging with structured clinical records. The model uses a multi-teacher training strategy to enhance generalizability and robustness across modalities, enabling more reliable representation learning for AD diagnosis.
resultsADLIP outperformed baseline CLIP and fine-tuned CLIP models in three-class classification and zero-shot diagnosis, achieving improved accuracy, F DISCUSSION: Our results demonstrate that contrastive multimodal representation learning enables clinically meaningful, generalizable, and temporally stable AD diagnosis across diverse populations. HIGHLIGHTS: A novel vision-language foundation model (Alzheimer's Disease Language and Image Pre-Training [ADLIP]) integrates 3D magnetic resonance imaging and clinical text for Alzheimer's disease diagnosis. ADLIP enables zero-shot prediction of unseen data and cognitive scores without task-specific fine-tuning. The model demonstrates strong generalizability across racially diverse cohorts, supporting equitable clinical use. Longitudinal evaluation shows alignment with disease progression, highlighting utility for monitoring applications.
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