Artificial intelligence in blood donor management: A narrative review

Area of Science:

• Biomedical Informatics
• Health Services Research

Background:

• Maintaining an adequate and safe blood supply is a critical healthcare challenge.
• Traditional blood donation management faces limitations in efficiency and donor engagement.

Purpose of the Study:

• To explore the integration of artificial intelligence (AI) technologies in improving blood donor recruitment, retention, and management.
• To identify the potential benefits and challenges of AI adoption in blood banking.

Main Methods:

• Review of AI applications including robotic process automation, machine learning (ML), natural language processing, and AI-driven chatbots.
• Analysis of AI's role in optimizing workflows, predicting donation patterns, forecasting demand, and enhancing donor communication.

Main Results:

• AI can streamline administrative tasks, enable intelligent decision-making, and improve efficiency in donor management.
• ML algorithms enhance predictive capabilities for donor behavior and blood demand, aiding in resource planning.
• AI-driven chatbots improve donor communication and retention, while AI supports rare donor identification.

Conclusions:

• AI offers significant potential to revolutionize blood banking operations, from donor management to supply chain optimization.
• Successful implementation requires addressing challenges related to data quality, ethical considerations, technical expertise, infrastructure, and cybersecurity.