Abstract: Individualized precision assessment of stored red blood cell (RBC) quality is a key requirement for advancing precision transfusion practices. The theoretical basis for this lies in the heterogeneity of red blood cells at the microscopic rheological, metabolic, and morphological levels, which leads to the formation and progression of storage lesions displaying "non-uniform" characteristics. Traditional quality control primarily relies on macro-indicators based on population-average storage duration, facing challenges in clinical translation including insufficient sensitivity, difficulty in identifying vulnerable subpopulations, and the need for invasive sampling. To overcome these limitations, this paper systematically reviews the latest developments in intelligent devices and artificial intelligence-assisted systems for individualized RBC quality assessment, with particular focus on microfluidic chip platforms, spatially offset Raman spectroscopy (SORS), and photoacoustic imaging technologies. Concurrently, deep learning and machine learning can perform automatic characterization and classification learning of microscopic morphological features, further establishing data-driven quality assessment frameworks. Although current challenges remain—including non-standardized clinical release criteria, insufficient biosafety assessment of nanosensing substrates, and cost-benefit imbalances—future prospects remain promising. By integrating low-cost, non-invasive point-of-care testing (POCT) with artificial intelligence, decision pathways can be established to promote the formation of intelligent, convenient, and clinically accessible quality assessment closed-loop systems. This approach will provide both theoretical foundation and practical guidance for overcoming the current bottleneck in storage lesion assessment.

Key words: Red blood cell storage lesion, Precision transfusion medicine, Microfluidic chips, Non-destructive in situ monitoring, Artificial neural networks