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Multimodal approaches leveraging neuroimaging and clinical data offer promise but often struggle with effective integration and generalization.<\/jats:p>\n <\/jats:sec>\n \n METHODS<\/jats:title>\n We developed Alzheimer's Disease Language and Image Pre\u2010Training (ADLIP), a vision\u2013language framework that integrates 3D T1\u2010weighted magnetic resonance imaging with structured clinical records. The model uses a multi\u2010teacher training strategy to enhance generalizability and robustness across modalities, enabling more reliable representation learning for AD diagnosis.<\/jats:p>\n <\/jats:sec>\n \n RESULTS<\/jats:title>\n \n ADLIP outperformed baseline CLIP and fine\u2010tuned CLIP models in three\u2010class classification and zero\u2010shot diagnosis, achieving improved accuracy, F\n 1<\/jats:sub>\n score, and macro\u2010area under the curve. Longitudinal evaluation demonstrated alignment with clinical trajectories. ADLIP maintained performance across racial subgroups without retraining.\n <\/jats:p>\n <\/jats:sec>\n \n DISCUSSION<\/jats:title>\n Our results demonstrate that contrastive multimodal representation learning enables clinically meaningful, generalizable, and temporally stable AD diagnosis across diverse populations.<\/jats:p>\n <\/jats:sec>\n \n Highlights<\/jats:title>\n \n \n \n A novel vision\u2013language foundation model (Alzheimer's Disease Language and Image Pre\u2010Training [ADLIP]) integrates 3D magnetic resonance imaging and clinical text for Alzheimer's disease diagnosis.<\/jats:p>\n <\/jats:list-item>\n \n ADLIP enables zero\u2010shot prediction of unseen data and cognitive scores without task\u2010specific fine\u2010tuning.<\/jats:p>\n <\/jats:list-item>\n \n The model demonstrates strong generalizability across racially diverse cohorts, supporting equitable clinical use.<\/jats:p>\n <\/jats:list-item>\n \n Longitudinal evaluation shows alignment with disease progression, highlighting utility for monitoring applications.<\/jats:p>\n <\/jats:list-item>\n <\/jats:list>\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/alz.71029","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T05:50:01Z","timestamp":1766987401000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A vision\u2013language foundation model for Alzheimer's disease diagnosis using MRI and clinical data"],"prefix":"10.1002","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2769-6293","authenticated-orcid":false,"given":"Ping\u2010Ju","family":"Lin","sequence":"first","affiliation":[{"name":"Laboratory of Neuro Imaging USC Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University of Southern California Los Angeles California 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