Machine Learning and Multimodal Biomarker Discovery in Alzheimer’s Disease

Abstract

Highlights What are the main findings? Machine learning improves Alzheimer’s disease detection by integrating blood, cerebrospinal fluid, imaging, genetic, transcriptomic, digital, sleep, and microbiome biomarkers. Multimodal and interpretable models generally outperform single-biomarker approaches and can support early diagnosis, disease staging, biomarker prediction, and patient stratification. What are the implications of the main findings? AI-assisted multimodal platforms could enable more accessible, minimally invasive, and personalized Alzheimer’s disease screening and clinical decision-making. Clinical translation will require longitudinal validation, diverse populations, standardized analytical pipelines, model interpretability, and appropriate regulatory oversight. Abstract Background/Objectives: The accelerating integration of machine learning (ML) with molecular, imaging, and physiological data is transforming Alzheimer’s disease (AD) research. Methods & Results: 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. Conclusions: 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.