Extracellular vesicles (EVs) are a type of cell-released phospholipid bilayer nanoscale carrier. However, research on EVs encounters several challenges, such as their heterogeneity, the complexities associated with their isolation and identification, the necessity for engineering optimization, and the limitations in exploring their mechanisms. The advancement of artificial intelligence (AI) technologies offers new opportunities for EV research. Here, the definition and brief history of AI, as well as types and common models of machine learning, are first introduced, and the interactions between AI, machine learning, and deep learning are explored. The article then discusses in detail a variety of applications of AI in EV research, including the use of AI for target identification and selective delivery of EVs, the design and optimization of drug delivery systems, the mapping of cellular communication networks, the analysis of multi-omics data, and synthetic biology-based research on EVs. These applications demonstrate the potential of AI in advancing EV research and applications. Finally, we offer an outlook on the major challenges and future prospects of AI. Overall, the introduction of AI technologies has provided new perspectives and tools for the study of EVs, which is expected to enhance the application of EVs in disease diagnosis and treatment.
Lü et al. (Tue,) studied this question.
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