Severe bleeding is one of the most common and preventable causes of death after traumatic injury, yet currently available tools have poor ability to determine which patients urgently need blood transfusions. A new multinational study, just published in Lancet Digital Health, suggests artificial intelligence (AI) may help close that gap.

Researchers have developed and validated machine-learning models that can accurately predict whether trauma patients will require blood transfusions, using only information available before they reach the hospital such as vital signs, injury patterns, and medication history.

Co-author Prof Patricia Maguire from University College Dublin (UCD), Director of UCD AI Healthcare Hub and UCD Institute for Discovery, said, “These findings show that AI-driven decision support could enable earlier and more precise identification of patients at highest risk of hemorrhagic shock, using data already available to emergency services. This has clear potential to support more timely transfusion decisions, although prospective evaluation will be needed before clinical implementation.”