Machine Learning and Multimodal Biomarker Discovery in Alzheimer's Disease

The accelerating integration of machine learning (ML) with molecular, imaging, and physiological data is transforming Alzheimer's disease (AD) research.

Recent studies demonstrate that multimodal, AI-assisted platforms can enhance early diagnosis, predict biomarker trajectories, and identify novel therapeutic targets. This mini-review covers the evolving AD diagnostic and biomarker frameworks, current therapeutic strategies including recently approved anti-amyloid immunotherapies, and advances from contemporary studies employing ML across diverse data streams, ranging from cerebrospinal fluid (CSF) and plasma proteomics to Raman spectroscopy, neuroimaging, transcriptomics, and microbiome signatures.

Collectively, they illustrate how artificial intelligence (AI) has shifted A biomarker discovery from univariate to network-based inference, achieving clinically relevant accuracy while emphasizing model interpretability. We discuss biological insights, translational implications, and persisting challenges related to validation, bias, and regulatory integration.