arXiv:2512.10966v3 Announce Type: replace-cross Abstract: Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary information from multimodal neuroimaging data. However, conventional fusion approaches often rely on simple concatenation of features, which cannot adaptively balance the contributions of biomarkers such as amyloid PET and MRI across brain regions. In this work, we propose MREF-AD, a Multimodal Regional Expert Fusion model for AD diagnosis. It is a Mixture-of-Experts (MoE) framework that models mesoscopi
The increasing availability of multimodal neuroimaging data and advancements in AI architectures capable of integrating complex datasets facilitate novel approaches to disease diagnosis.
Improved early and accurate diagnosis of Alzheimer's disease via AI-driven multimodal fusion can significantly impact patient outcomes, healthcare costs, and research directions for neurodegenerative diseases.
Traditional diagnostic methods for Alzheimer's become less competitive as AI-powered systems offer more precise and interpretable insights by adaptively weighing different biomarkers and brain regions.
- · AI healthcare startups
- · Medical imaging companies
- · Neuroscience researchers
- · Patients with neurodegenerative diseases
- · Traditional diagnostic labs
- · Pharmaceutical companies focused solely on late-stage AD treatments
Earlier and more frequent diagnosis of Alzheimer's disease becomes possible, enabling proactive intervention strategies.
The demand for personalized medicine and targeted therapies for neurodegenerative conditions will increase due to improved diagnostic precision.
This model could be adapted to diagnose other complex diseases requiring the fusion of diverse biological data, accelerating AI's role in multi-omic diagnostics.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI