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Doctors have developed an AI tool that could reduce wasted efforts to transplant organs by 60%.

Thousands of patients worldwide are waiting for a potentially life-saving donor, and more candidates are stuck on waiting lists than there are available organs.

Recently, in cases where people need a liver transplant, access has been expanded by using donors who die after cardiac arrest. However, in about half of these donations after circulatory death (DCD) cases, the transplant ends up being cancelled.

That is because the time between the removal of life support and death must not exceed 45 minutes. If the donor does not die within the timeframe needed to preserve organ quality, surgeons often reject the liver because of the increased risk of complications to the recipient.

Now doctors, scientists and researchers at Stanford University have developed a machine learning model that predicts whether a donor is likely to die within the timeframe during which their organs are viable for transplantation.

The AI tool outperformed the judgment of top surgeons and reduced the rate of futile procurements – which occur when transplant preparations have begun but the donor dies too late – by 60%.

Read full article.

Source: The Guardian (13 November 2025)

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