The Future of Blood Banking: Molecular Typing, Artificial Intelligence, and Predictive Transfusion Safety | ||
| Journal of Medical and Life Science | ||
| Articles in Press, Corrected Proof, Available Online from 20 November 2025 | ||
| DOI: 10.21608/jmals.2025.466701 | ||
| Authors | ||
| Mohammed Ali Shekiri* 1; Ibrahim Abdelaziz Ghoneim1; Maram Hassan Alharbi1; Mohammed Motlaq Alamri1; Naseem Olythah AlAhmadi1; Hind Hezam Almutairi1; Abdullah Ali Alraddadi1; Mohamed Saleem Haroun2 | ||
| 1Prince Sultan Armed Forces Hospital, Al Madinah Al Munawwarah, Saudi Arabia | ||
| 2Medical Laboratories, Prince Sultan Armed Forces Hospital, Medina | ||
| Abstract | ||
| Background: Modern transfusion medicine is evolving from traditional serology to molecular and data-driven approaches. However, despite such advances, complications such as alloimmunization and hemolytic reactions continue to pose a major risk for patient safety. Aim: This review aims to collate recent advances in molecular typing, artificial intelligence, and predictive analytics that together redefine transfusion safety, operational efficiency, and the very philosophy of transfusion practice in blood banks. Methods: A narrative review was carried out using the databases PubMed, Scopus, and ScienceDirect. Articles were selected that focused on molecular blood group genotyping, machine learning applications in transfusion medicine, and predictive safety modeling. Only peer-reviewed studies published in English between 2019 and 2025 were considered for the study. Results: Key findings include the fact that molecular typing, especially NGS, provides much higher accuracy in antigen identification compared to serology. This allows for very accurate donor-recipient matching and proactive prevention of alloimmunization. Meanwhile, AI is used in antibody screening, compatibility predictions, and workflow automation, reducing human error. Predictive analytics, too, in conjunction with laboratory information systems and electronic health records, demonstrates considerable promise in predicting risks such as delayed hemolytic reactions in advance of clinical manifestations. Conclusion: Integration of molecular genomics with artificial intelligence presents the future for blood banking in the form of a predictive, personalized transfusion model. This convergence will enhance patient safety, reduce costs, and transform the blood bank from a reactive service into an intelligent, proactive healthcare node. | ||
| Keywords | ||
| molecular blood group genotyping; artificial intelligence in transfusion; predictive analytics; alloimmunization prevention; smart blood bank | ||
|
Statistics Article View: 58 |
||
