AI Applications in Transfusion Medicine: Opportunities, Challenges, and Future Directions

Area of Science:

• Healthcare Technology
• Medical Informatics
• Hematology

Background:

• Transfusion medicine faces persistent challenges in efficiency, safety, and resource allocation.
• Artificial intelligence (AI) offers novel solutions to these longstanding issues.
• AI integration, including Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), and predictive analytics, is rapidly advancing.

Purpose of the Study:

• To review the diverse applications of AI in transfusion medicine.
• To highlight AI's potential to enhance operational efficiency and patient outcomes.
• To discuss challenges and future directions for AI implementation in this field.

Main Methods:

• Review of current AI applications in transfusion medicine domains.
• Analysis of AI's role in donor management, blood product optimization, and risk prediction.
• Exploration of AI in antigen phenotyping, hemovigilance, and inventory management.

Main Results:

• AI demonstrates potential in improving donor management, blood product quality, and predicting transfusion needs.
• AI tools enhance patient safety by assessing bleeding risks and enabling accurate blood antigen phenotyping.
• AI streamlines operations through automated hemovigilance and advanced inventory forecasting.

Conclusions:

• AI integration shows significant promise for improving patient outcomes and advancing precision medicine in transfusion services.
• Addressing challenges like algorithmic transparency, data privacy, bias, and equitable access is crucial for responsible AI adoption.
• Future research should focus on scalable AI models, federated learning, and AI-driven clinical trials to redefine transfusion medicine globally.