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

Area of Science:

• Biochemistry
• Oncology
• Bioinformatics

Background:

• Exosomes are extracellular vesicles carrying molecular signals from cells.
• In oncology, exosomes serve as non-invasive liquid biopsies for monitoring tumors.
• Biofluid complexity challenges traditional exosome isolation and analysis.

Purpose of the Study:

• To review how AI, including machine learning and deep learning, extracts clinical insights from exosome data.
• To highlight AI's role in improving exosome isolation and biomarker discovery.
• To discuss AI's potential in predicting treatment responses and enabling personalized medicine.

Main Methods:

• Review of AI applications in exosome research.
• Analysis of machine learning and deep learning techniques for exosome data.
• Integration of multi-omics and single-exosome analysis with AI.

Main Results:

• AI enhances exosome isolation and biomarker identification for disease-specific detection.
• AI models predict therapeutic responses with high precision.
• AI facilitates early cancer detection and monitoring of treatment resistance.

Conclusions:

• AI integration with exosome biology promises a shift towards proactive, personalized cancer care.
• AI-driven exosome analysis offers a framework for managing complex diseases beyond cancer.
• Standardization and data privacy are key challenges for AI in exosome research.