Spotlights on novel strategic innovations on the artificial intelligence and deep learning driven quality control focuses in transfusion medicine, to optimize blood component safety and efficacy and minimize the potential pitfalls.
Journal:
Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis
Published Date:
Jun 1, 2025
Abstract
Artificial intelligence (AI) combined with human intelligent, and machine learning (ML) are transforming quality control (QC) in transfusion medicine, enhancing efficiency, accuracy, and transfusion clinical safety. Traditional QC methods require extensive monitoring and manual analysis, leading to delays and variability. AI-driven QC automates metrological device calibration, internal quality control (IQC), and statistical process control (SPC), enabling real-time monitoring, predictive analytics, and proactive error mitigation. AI enhances measurement reliability, optimizes QC protocols, and minimizes false rejections, ensuring compliance with transfusion medicine regulations. In blood component manufacturing, ML predicts storage stability, donor suitability, and pathogen risks, improving transfusion safety. Ethical and regulatory compliance remains essential for AI adoption, ensuring transparency, bias mitigation, and data security. With future advancements in deep learning, block chain, and AI-driven logistics, AI will play a crucial role in enhancing QC, optimizing resources, and ensuring safety of blood derived products for clinical use are optimized and their potential pitfalls are minimized leading to more effective transfusion medicine and regenerative medicine.
