Dementia-Agents: A Multi-Modal Multi-Agent System for Dementia Staging and Phenotyping

• URL: http://arxiv.org/abs/2606.21168v1
• Date: Fri, 19 Jun 2026 07:16:36 GMT
• Title: Dementia-Agents: A Multi-Modal Multi-Agent System for Dementia Staging and Phenotyping
• Abstract summary: Dementia-Agents is a clinically aligned multi-agent framework for dementia staging and phenotyping.<n>We develop and evaluate Dementia-Agents on a real-world clinical cohort of 1,066 patients.
• Score: 13.18271034396111
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Dementia diagnosis requires integrating multi-modal clinical assessments from diverse informants and clinicians under incomplete and heterogeneous data conditions. Yet most AI-driven approaches remain Alzheimer's disease (AD)-centric, framing the problem as binary AD detection or three-stage AD progression modeling within well-curated research settings. This pathology-driven paradigm overlooks the broader, syndrome-level nature of dementia, which spans multiple stages, phenotypes, and etiologies. In this paper, we propose Dementia-Agents, a clinically aligned multi-agent framework for real-world dementia staging and phenotyping. The framework follows a three-step workflow: (1) a data agent translates structured clinical records into semantically faithful textual representations that preserve missing-data signals and routes them to domain-aligned experts; (2) five fine-tuned expert agents generate domain-level predictions; and (3) a coordinator agent performs probabilistic aggregation to produce final staging and phenotyping decisions. We develop and evaluate Dementia-Agents on a real-world clinical cohort of 1,066 patients from two cognitive neurology services. Compared with monolithic multi-modal large language models (MLLMs) and prior medical multi-agent systems, our approach achieves consistent improvements in diagnostic performance for real-world syndrome-level dementia staging and phenotyping, while preserving domain-level interpretability.

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