Abstract

The emergence of AI in medical practice is revolutionizing clinical practices, specifically for the separation of blood components from apheresis. There is growing evidence that AI enhances the accuracy and effectiveness of apheresis through real-time data analytics, predictive modeling and advance decision support systems. Machine learning algorithms use patient-specific characteristics to improve working conditions, personalize treatment routines and predict potential side effects; since they provide the necessary preventative care. Artificial intelligence helps to improve medical service by automatically monitoring and documenting data, which can help relieve the mental burden of clinicians and administrative pressure. Enhanced pattern recognition and anomaly detection helps with quality control and early detection of equipment failures or biological irregularities. In therapeutic apheresis, AI modalities augment evidence-based medicine by combining clinical datasets, laboratory results, procedural measures, and patient outcomes. This combination raises evidence-based recommendations to help guide treatment decisions and improve patient outcomes. Despite these accomplishments, many challenges persist such as data privacy concerns, interfacing with legacy systems, and the need for tight regulatory oversight. However, gradual convergence of AI and apheresis has significant potential to improve patient care, organization productivity, and therapeutics effectiveness. Expected developments will further extend AI’s role in diagnostics, prediction, and apheresis process automation and remote monitoring. In this talk we emphasize the potential of AI for advancing safety, personalization, and efficiency in state-ofthe-art apheresis.

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