Application of AI-assisted technology in resolving difficult problems of blood transfusion compatibility detection
10.13303/j.cjbt.issn.1004-549x.2025.11.002
- VernacularTitle:人工智能辅助技术在处理输血相容性检测疑难问题中的应用研究
- Author:
Fengxia LIU
1
;
Jiashun GONG
1
;
Rong HUANG
1
;
Xueyuan HUANG
1
;
Hang DONG
1
;
Rong GUI
1
Author Information
1. Department of Blood Transfusion, the Third Xiangya Hospital of Central South University, Changsha 410013, China
- Publication Type:Journal Article
- Keywords:
blood transfusion compatibility detection;
AI-assisted technology;
blood transfusion safety;
precise blood transfusion
- From:
Chinese Journal of Blood Transfusion
2025;38(11):1477-1487
- CountryChina
- Language:Chinese
-
Abstract:
Objective: Through analyzing the current handling capabilities for complex cases in blood transfusion compatibility detection and the application status of artificial intelligence-assisted (AI-assisted) technologies, this study explores the establishment of an effective AI-augmented protocol for managing challenging blood transfusion compatibility detection. Methods: This research systematically analyzes, designs, and explores an AI-augmented operational workflow for resolving challenging blood transfusion compatibility detection cases. Through three representative case studies, we evaluate its effectiveness, accuracy, and efficiency in addressing real-world diagnostic challenges. Results: The AI-augmented operational model demonstrates significant efficacy in resolving complex blood transfusion compatibility challenges, including complex blood typing, antibody specificity identification, challenging cross-matching, and transfusion strategy formulation. Conclusion: AI-augmented technologies demonstrate immense potential in resolving complex blood transfusion compatibility detections. By enabling intelligent, automated, precise, and standardized solutions, they significantly enhance diagnostic accuracy and operational efficiency, which is critical for ensuring transfusion safety and advancing personalized transfusion medicine. This study delineates both the advantages and existing limitations of AI implementation, explores future developmental trajectories, and provides a theoretical framework and practical implementation pathways for deeper integration of AI in transfusion medicine.
