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. 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.
