# Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine

## TL;DR

This paper reviews how AI and multimodal neuroimaging can improve diagnosis and treatment of cognitive disorders like dementia, while addressing challenges and future directions.

## Contribution

The paper provides a comprehensive review of multimodal AI frameworks for cognitive disorders, highlighting novel fusion strategies and future research directions.

## Key findings

- Multimodal AI improves differential diagnosis and early detection of dementia through presymptomatic biomarkers.
- Explainable AI (XAI) techniques enhance transparency in clinical applications of AI models.
- Federated learning and advanced XAI are proposed to address data scarcity and interpretability challenges.

## Abstract

Cognitive disorders, with dementia as a primary exemplar, present profound diagnostic and therapeutic challenges due to their complex pathologies and heterogeneous presentations. 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’s dementias and prodromal stages—exacerbated by demographic biases—and 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.