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Blood transfusions are vital in health care, yet maintaining an adequate and safe blood supply remains a significant challenge. To address blood donation-associated challenges, this review explores how integrating artificial intelligence (AI) technologies can improve donor recruitment, retention and management. For instance, robotic process automation can streamline repetitive administrative tasks, allowing staff to focus on more critical activities and improving efficiency. When augmented with AI techniques such as machine learning (ML) and natural language processing, it transitions from static rule-based automation to intelligent process automation. This combination enables dynamic decision making, handling unstructured data and optimizing workflows, thus extending its role in improving efficiency and decision making in donor management. ML algorithms can analyse large datasets to predict future donation patterns, identify donor behaviour trends and forecast blood demand more accurately. By applying these predictive models, blood banks can plan more effectively, avoid shortages and precisely target recruitment efforts. Additionally, AI-driven chatbots are gaining traction as a tool for improving communication with potential and existing donors, ultimately fostering better retention rates. Beyond routine donor management, AI also shows promise in supporting rare donor identification and targeted engagement strategies. While these innovations hold great potential, their implementation faces challenges such as data availability and quality, ethical issues concerning AI utilization, the necessity for clinical and technical expertise, a robust infrastructure, environmental impact and cybersecurity risks. Addressing these issues through practical strategies and thoughtful integration will be the key to ensuring the responsible, effective and sustainable adoption of AI in blood banking systems.
Maha A. Badawi (Tue,) studied this question.