Nanodrug delivery systems (NDDSs) have demonstrated broad application prospects in disease treatment, prevention, and diagnosis due to their nanoscale size advantages and high drug-loading capacity. However, their clinical translation still faces multiple challenges, including rapid clearance by the reticuloendothelial system (RES), nonspecific targeting, and insufficient efficiency in crossing biological barriers. Cell membrane-coated biomimetic delivery systems (CMC-BDS), which integrates natural cell membranes onto nanoparticle (NPs) surfaces, provides nanodrugs with a versatile "biomimetic cloak," representing a highly promising surface engineering strategy. This approach enables nanocarriers to inherit the intrinsic biological properties of different cell sources, endowing them with immune evasion, prolonged circulation, dynamic targeting, biocompatibility, and biodegradability, while supporting the integration of diverse biomedical functions. Furthermore, surface functionalization modifications can enhance their programmability, multifunctionality, and biointerface adaptability, thereby optimizing targeted delivery efficiency and extending in vivo circulation time. This review first outlines the development and key preparation steps of cell membrane coating technology. It then discusses the selection strategies for various cell membrane types-including leukocyte, erythrocyte, platelet, dendritic cell, tumor cell, and bacterial membranes-while comparing their respective advantages and limitations. Finally, the review highlights recent advances in applying cell membrane-coated nanoparticles (CMC-NPs) for treating tumors, ischemic stroke, and inflammatory diseases.

Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.