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Artificial intelligence (AI), particularly when applied to multimodal neuroimaging and clinical data, offers a powerful approach to advancing precision medicine in this domain. This comprehensive review first examines foundational AI algorithms, including artificial neural networks for feature extraction, multimodal fusion strategies (e.g. early, intermediate, and late fusion) for data integration, and explainable AI (XAI) techniques to enhance clinical transparency. The core focus is on the application of these multimodal AI frameworks across the dementia care continuum, encompassing improved differential diagnosis, early detection through presymptomatic biomarkers, development of predictive models for disease progression, and optimization of patient stratification for clinical trials. Despite significant advances, persistent challenges remain, including limited generalizability across populations and protocols, data scarcity for non-Alzheimer\u2019s dementias and prodromal stages\u2014exacerbated by demographic biases\u2014and barriers to interpretability. We discuss solutions such as federated learning for privacy-preserving data sharing and advanced XAI techniques. Finally, we outline pivotal future directions, including intelligent sensor fusion for discovering novel early biomarkers, hybrid AI architectures combining generative and discriminative models, innovations for handling missing modalities, and robust multicenter data integration frameworks. By synthesizing these advances, this review highlights the role of multimodal AI in advancing precise diagnosis, early prediction, and therapeutic development for neurodegenerative and vascular cognitive disorders, while identifying key translational challenges for precision medicine.<\/jats:p>","DOI":"10.1093\/psyrad\/kkag007","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T12:43:24Z","timestamp":1773146604000},"source":"Crossref","is-referenced-by-count":8,"title":["Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine"],"prefix":"10.1093","volume":"6","author":[{"given":"Mingxi","family":"Dang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University , Beijing 100875 ,","place":["China"]},{"name":"School of Systems Science, Beijing Normal University , Beijing 100875 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