Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment

TL;DR

Cerebra is an interactive, multi-agent AI system that integrates multimodal clinical data to improve dementia risk assessment and diagnosis, providing interpretable and robust decision support for clinicians.

Contribution

This work introduces Cerebra, a novel multi-agent AI framework that coordinates heterogeneous data analysis and enhances clinical decision-making in dementia care.

Findings

Outperformed state-of-the-art single-modality and language models in dementia prediction.

Achieved AUROC up to 0.80 for risk prediction and 0.86 for diagnosis.

Significantly improved physician accuracy by 17.5 percentage points in a reader study.

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 massive multi-institutional dataset spanning 3 million patients from four independent healthcare systems. Cerebra consistently outperformed both state-of-the-art single-modality models and large multimodal language model baselines. In dementia risk prediction, it achieved AUROCs up to 0.80, compared with 0.74 for the strongest single-modality model and 0.68 for language model baselines. For dementia diagnosis, it achieved an AUROC of 0.86, and for survival prediction, a C-index of 0.81. In a reader study with experienced physicians, Cerebra significantly improved expert performance, increasing accuracy by 17.5 percentage points in prospective dementia risk estimation. These results demonstrate Cerebra's potential for interpretable, robust decision support in clinical care.