Stanford-Led AI Model Improves Efficiency of Liver Transplant Decisions

Key Details

• 1A machine-learning model predicts whether donors will die within 30–45 minutes after life support removal, enabling timely liver transplantation.
• 2The model was trained on over 2,000 cases from six US transplant centers and outperformed surgeons’ judgment (75% vs 65% accuracy).
• 3Futile procurement rates (resources spent without a successful transplant) dropped by 60% using the model.
• 4Missed transplant opportunities remained similar to surgeon judgment (just over 15%), but further model refinements lowered this to ~10% in recent tests.
• 5The model incorporates donor demographics, vitals, lab results, ventilator settings, and neurological assessments, and features a natural language interface for clinical data extraction.
• 6It is customizable to clinical and hospital preferences and is being adapted for heart and lung transplantation.