Artificial Intelligence in Hemovigilance: Advancing Blood Safety and Monitoring Systems
Ragesh Gurumoorthy1★, Karthik Thiyagarajan2, Santhosh Kumar Ganesan2★ Corresponding author
- 1Chettinad School of Pharmaceutical Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, Tamil Nadu, INDIA.
- 2Department of Pharmacy Practice, Sri Ramachandra Faculty of Pharmacy, Sri Ramachandra Institute of Higher Education and Research, Ramachandra Medical College, Sri Ramachandra Nagar, Porur, Chennai, Tamil Nadu, INDIA.
CORRESPONDENCE
Ragesh Gurumoorthy
Chettinad School of Pharmaceutical Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, Tamil Nadu, INDIA.
Received: 09-06-2025; Revised: 18-07-2025; Accepted: 22-09-2025.
Volume 17, Issue 4 · pp. 770–776 · PUBLISHED · DOI: 10.5530/jyp.20251751
ABSTRACT
Blood transfusion is one of the key practices in making sure that the safety of the patients remains intact; however, traditional systems are being faced with a substantial number of challenges, such as underreporting utility of data integration issues and slow response time. Artificial Intelligence (AI) technology brings new light in dealing with these problems by means of the integration of advanced information systems that could differentiate, find out, and report the transfusion cases. This review discusses the potential of emerging AI in advancing hemovigilance by way of integrating natural language processing to support data integration, machine learning models for the detection of adverse events, and predictive analytics for personalised risk management. AI technologies are further implemented in the supply chain to improve the blood supply by the application of optimisation techniques of demand forecasting and waste reduction. It is important to note that even though hemovigilance systems' AI-based solutions ensure safe patient care and efficient operation, there are always some problems faced by AI-based technologies, for example, data privacy, algorithmic biases, and inadequate regulatory frameworks, which might cause unnecessary obstructions. In the future, effective privacy-enhancing methods will be developed, like federated learning, in addition to Explainable AI (XAI) for the human building of robust, transparent, and secure AI hemovigilance systems. This review stresses that AI will play a vital role in the future of transfusion by improving the safety and efficiency of medical services eventually.
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Gurumoorthy, R., Thiyagarajan, K., & Ganesan, S. K. (). Artificial Intelligence in Hemovigilance: Advancing Blood Safety and Monitoring Systems. Journal of Young Pharmacists, 17(4), 770–776. https://doi.org/10.5530/jyp.20251751