Integrating artificial intelligence with nanodiagnostics for early detection and precision management of neurodegenerative diseases

Abstract

BACKGROUND: Neurodegenerative diseases—including Alzheimer’s, Parkinson’s, and amyotrophic lateral sclerosis (ALS)—as well as autoimmune disorders with neurodegenerative features such as multiple sclerosis (MS), present an escalating global challenge. Current diagnostics often detect pathology too late, and most treatments focus on symptom relief rather than disease modification. There is an urgent need for tools that enable early detection and precision-targeted intervention. MAIN BODY: Nanotechnology offers unique advantages in this space, enabling early molecular detection, targeted drug delivery, and theranostic systems. Engineered nanocarriers, biosensors, and responsive nanodevices are being tailored to disease-specific features such as oxidative stress in AD or neuroinflammation in MS. Yet, issues like biocompatibility, clinical scalability, and long-term safety remain barriers to translation. Artificial intelligence (AI) enhances nanomedicine by improving biomarker sensitivity, stratifying patients, and enabling predictive disease modeling. From AI-guided nanoparticle design to closed-loop delivery systems and digital twin models, these technologies work synergistically to support real-time, personalized care. Still, critical challenges—including algorithmic bias, lack of explainability, heterogeneous datasets, and limited regulatory clarity—impede clinical integration. Additionally, high system complexity and cost risk excluding low-resource settings unless inclusive, scalable alternatives are pursued. CONCLUSION: The convergence of AI and nanotechnology is reshaping neurodegenerative disease care, moving from reactive to proactive, personalized neurology. Realizing this promise requires cross-sector collaboration, ethical foresight, and translational rigor to ensure these innovations are safe, equitable, and accessible to all patients.

Topics & Concepts