AI Case Study
The U.S. Organ Procurement and Transplantation Network is saving lives by matching kidney donors and recipients using algorithms built by AI researchers
The U.S. Organ Procurement and Transplantation Network has leveraged AI to optimise kidney allocations for transplants. Algorithms are trained to scan the database of registered kidney patients and their partnered donors and identify matches. The hardest to match patients would be prioritise, while all matches are firstly made on the basis of biological suitability.
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"Now, thanks to artificial intelligence, a person stepping forward to donate a kidney to a loved one—or to a perfect stranger—can set off a chain that saves dozens of lives.
Paired kidney donation is one of the great success stories of artificial intelligence. It doesn’t eliminate jobs or scrub the human touch from medical care. It takes an incredibly complex problem and solves it faster and with fewer errors than humans can, and as a result saves more lives. Since the first paired kidney exchange surgeries took place in 2000, nearly 6,000 people have received kidney transplants from paired exchanges identified by algorithms. Today roughly one in eight transplant recipients who receive a kidney from a living donor are matched with that person through paired exchange.
At the same time, paired kidney exchange is also a perfect example of AI’s limitations. A computer can only do what a human can teach it, and we can’t teach what we don’t understand. In the decades since medicine learned how to replace a failing kidney with a donated one, we are still struggling with the problem of how to distribute the precious few kidneys available in a way that feels fair and satisfactory to everyone, and doesn’t result in undesirable, unintended consequences. AI can identify potential donors and recipients who are biologically suited for one another; in the future, it may even be able to weigh the moral factors that determine who gets a transplant first. But first, we humans have to agree on what those should be.
The kidneys act as the body’s filters. For people with kidney failure, dialysis essentially replicates the organs’ function externally, removing a patient’s unfiltered blood over a period of hours and pumping it back in. The invention of dialysis in the middle of the 20th century turned a disease that was once a death sentence into a chronic but manageable condition.
Today, multiple US hospitals run their own paired kidney donation programs. There are also three larger US exchanges that organize kidney chains across hospitals: the United Network for Organ Sharing, the National Kidney Registry, and the Alliance for Paired Kidney Donation. National exchanges are in place in the UK, Canada, and the Netherlands, and paired donations have taken place in hospitals from India to South Africa. Researchers have also theorized that similar exchanges are possible for lung and partial liver transplants, though no system for such swaps is yet in place.
Today, when doctors are looking to match kidney donors and recipients, algorithms built by AI researchers trawl the database of registered kidney patients and their partnered donors, and identify matches based on a list of weighted criteria hammered out by a committee of the Organ Procurement and Transplantation Network and the United Network for Organ Sharing, which together oversee US organ transplants.
These algorithms have facilitated thousands of life-saving surgeries. And in the future, it could be possible for an AI not just to make matches using the criteria that humans have decided upon, but to actively participate in that judgment process—to understand human decision making and value systems such that it can make its own tie-breaking judgment calls about which kidneys should go where (a decision that would then be reviewed by human doctors). On this point, the limiting factor isn’t so much the technology as the people using it.
The algorithms evaluate all the transplants possible among the patient-donor pool at once. Matches are made primarily on biological suitability, with the hardest-to-match patients getting first priority. The technology weighs criteria including the time the recipient has been on the waiting list, his or her age (children get priority), and whether the person who needs a kidney has been a living organ donor in the past (with the reasoning that people who have stepped up to give before should get priority if they one day find themselves in need).
"There used to be only three ways off of a kidney transplant waiting list. The first was to find a healthy person from within one’s own pool of friends and family, who perfectly matched both the recipient’s blood and tissue types, and possessed a spare kidney he or she was willing to part with.
The second was to wait for the unexpected death of a stranger who was a suitable physical match and happened to have the organ-donor box checked on their driver’s license.
The third was to die.
But then it occurred to doctors: given enough kidney patients, and enough healthy, willing donors, they could form a pool big enough to facilitate far more matches than the one-to-one system of the past. As long as patients could procure a donor—any donor, even one that wasn’t a fit with the patient themselves—they could get a matching kidney.
At first, this required doctors to spend brain-searing hours poring over the details of blood types and tissue variations in patients’ and potential donors’ charts. Then computer scientists and economists got involved. They built algorithms that performed these complicated matches more elegantly than human brains ever could."
"Today roughly one in eight transplant recipients who receive a kidney from a living donor are matched with that person through paired exchange."
"database of registered kidney patients and their partnered donors"