Severe bleeding is a leading and preventable cause of death following traumatic injuries, yet existing tools struggle to identify patients in urgent need of blood transfusions. A recent study in Lancet Digital Health reveals that artificial intelligence (AI) could bridge this gap. Researchers developed machine-learning models capable of predicting blood transfusion needs based on pre-hospital data, including vital signs and injury patterns. Co-author Prof. Patricia Maguire from University College Dublin (UCD) highlighted the potential of AI in enhancing pre-hospital decision-making and identifying patients at risk of hemorrhagic shock. Analyzing data from 364,350 U.S. trauma patients and 54,210 from five other countries, the AI models demonstrated high accuracy in predicting transfusion requirements. This advancement could vastly improve emergency responses, allowing trauma teams to act swiftly. However, further validation and prospective trials are necessary to assess AI’s effectiveness in real-world clinical settings, as the current findings serve as a preliminary development phase.
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