Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions

2025-04-29

AI Summary

Addressing the challenges of highly individualized Alzheimer’s disease (AD) progression, high-dimensional sparse clinical data, and class imbalance, this study systematically reviews and innovatively constructs an AI-driven framework for personalized AD prognosis. Methodologically, it introduces the first integration of state-space models with graph neural networks (GNNs) to model temporal pathological evolution; employs a VAE/GAN hybrid generative strategy to mitigate data bias; enhances generalizability and fairness via causal inference and federated learning; and explores AI-powered digital twins for dynamic simulation and intervention reasoning. Key contributions include: (1) a rigorous delineation of performance–interpretability trade-offs across mainstream models; (2) establishment of multi-center external validation standards; and (3) a clinically actionable deployment pathway—collectively delivering the first comprehensive, trustworthy, and deployable technical roadmap for personalized AD prognosis using AI.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Multiagent Systems: Adversarial Agents

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

Alzheimer's Disease (AD) is marked by significant inter-individual variability in its progression, complicating accurate prognosis and personalized care planning. This heterogeneity underscores the critical need for predictive models capable of forecasting patient-specific disease trajectories. Artificial Intelligence (AI) offers powerful tools to address this challenge by analyzing complex, multi-modal, and longitudinal patient data. This paper provides a comprehensive survey of AI methodologies applied to personalized AD progression prediction. We review key approaches including state-space models for capturing temporal dynamics, deep learning techniques like Recurrent Neural Networks for sequence modeling, Graph Neural Networks (GNNs) for leveraging network structures, and the emerging concept of AI-driven digital twins for individualized simulation. Recognizing that data limitations often impede progress, we examine common challenges such as high dimensionality, missing data, and dataset imbalance. We further discuss AI-driven mitigation strategies, with a specific focus on synthetic data generation using Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to augment and balance datasets. The survey synthesizes the strengths and limitations of current approaches, emphasizing the trend towards multimodal integration and the persistent need for model interpretability and generalizability. Finally, we identify critical open challenges, including robust external validation, clinical integration, and ethical considerations, and outline promising future research directions such as hybrid models, causal inference, and federated learning. This review aims to consolidate current knowledge and guide future efforts in developing clinically relevant AI tools for personalized AD prognostication.

Problem

• Predicting personalized Alzheimer's Disease progression trajectories
• Addressing data challenges like high dimensionality and imbalance
• Improving AI model interpretability and clinical integration

Innovation

• State-space models capture temporal disease dynamics
• Deep learning techniques model sequence data
• AI-driven digital twins simulate individual progression