AI Tool Slashes Wasted Organ Transplant Efforts by 60%

In November 2025, Stanford researchers unveiled a groundbreaking AI tool that could significantly improve the efficiency of organ transplantation. The new machine learning model is capable of predicting whether a donor is likely to die within the critical 45-minute timeframe required to preserve organ quality for transplantation.

Currently, in about half of donations after circulatory death (DCD) cases, the transplant is ultimately cancelled because the donor does not die within the necessary timeframe. This leads to wasted efforts and resources for healthcare workers. However, the AI tool developed by the Stanford team has been shown to outperform top surgeons' judgments, reducing the rate of futile procurements by 60%.

"By identifying when an organ is likely to be useful before any preparations for surgery have started, this model could make the transplant process more efficient," explained Dr. Kazunari Sasaki, a clinical professor of abdominal transplantation and senior author on the study.

The AI tool was trained on data from over 2,000 donors across multiple US transplant centers. It uses neurological, respiratory, and circulatory data to accurately predict a donor's progression to death, allowing healthcare staff to make more informed decisions and optimize organ utilization. Importantly, the model maintains its accuracy even when some donor information is missing.

This breakthrough has the potential to be a game-changer in the field of organ transplantation, enabling more patients in need to receive life-saving procedures. The research team plans to further develop the AI tool to trial it with heart and lung transplants as well.