[2603.21597] A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment

Computer Science > Artificial Intelligence arXiv:2603.21597 (cs) [Submitted on 23 Mar 2026] Title:A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment Authors:Sheng Liu, Long Chen, Zeyun Zhao, Qinglin Gou, Qingyue Wei, Arjun Masurkar, Kevin M. Spiegler, Philip Kuball, Stefania C. Bray, Megan Bernath, Deanna R. Willis, Jiang Bian, Lei Xing, Eric Topol, Kyunghyun Cho, Yu Huang, Ruogu Fang, Narges Razavian, James Zou View a PDF of the paper titled A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment, by Sheng Liu and 18 other authors View PDF Abstract:Modern clinical practice increasingly depends on reasoning over heterogeneous, evolving, and incomplete patient data. Although recent advances in multimodal foundation models have improved performance on various clinical tasks, most existing models remain static, opaque, and poorly aligned with real-world clinical workflows. We present Cerebra, an interactive multi-agent AI team that coordinates specialized agents for EHR, clinical notes, and medical imaging analysis. These outputs are synthesized into a clinician-facing dashboard that combines visual analytics with a conversational interface, enabling clinicians to interrogate predictions and contextualize risk at the point of care. Cerebra supports privacy-preserving deployment by operating on structured representations and remains robust when modalities are incomplete. We evaluated Cerebra using a massi...