Artificial Intelligence-Enabled Exosomes in Precision Oncology: A Framework for Clinical Utility and Biomedical...

Exosomes are 30-150 nm extracellular vesicles that convey molecular information reflecting the physiological and pathological states of their source cells. In precision oncology, they function as a non-invasive "liquid biopsy," enabling real-time monitoring of tumor dynamics and metastasis. However, extreme biofluid heterogeneity poses significant challenges for their isolation and analysis using conventional statistical approaches. This review aims to examine how artificial intelligence (AI), specifically machine learning and deep learning, transforms complex exosomal "noise" into actionable clinical insights. AI enhances exosome isolation, enables disease-specific biomarker identification, and predicts therapeutic responses with high precision. Integrating multi-omics data and single-exosome analysis enables AI-driven models to facilitate early cancer detection and therapeutic resistance monitoring. Despite challenges related to standardization and data privacy, the convergence of AI and exosome biology is poised to transform reactive cancer treatments into a proactive, personalized medical ecosystem. This approach also provides a framework for managing other complex systemic diseases.