Artificial intelligence is ascendant in medicine—from AI eye doctors to chatbot therapists. As medical databases balloon in size and complexity, researchers are teaching computers to sift through and identify patterns, hinting at a future in which machine learning algorithms diagnose disease all on their own.

Sometimes, algorithms pick up on early signs of disease that humans wouldn't even know to look for. Last week, researchers at the University of North Carolina and Washington University reported an AI that can identify autistic infants long before they present behavioral symptoms. It's a thrilling opportunity: Early detection gives autism neuroscience a big leg up, as researchers try to understand what goes wrong during development. But now clinicians and researchers have to figure out what they’ll do with that information—is it just a research tool, or will they one day begin diagnosing and treating autism before symptoms start? Especially when it comes to infants, it won't be easy to entrust medical care to a computer-generated guess.

In this study, researchers scanned the brains of 59 6 month-olds whose older siblings were already diagnosed with autism. By age two, 11 of those infants had received a diagnosis of autism. By training a machine learning algorithm on their behavior and earlier MRI data, the scientists built a model that predicted 9 of those 11 autism cases, with no false positives. The AI predicted autism around a year before the earliest age—around 14 months—that clinicians diagnose it based on behavior.

Most immediately, this model can help researchers understand how the disorder develops and find early interventions for autism. Right now, researchers tracking autism development focus on infant siblings of people with autism; they have 1 in 5 chance of developing autism, compared to around 1 in 100 for the general population. With machine learning, though, researchers could scan infant siblings and focus their research on those at the very highest risk, buying more statistical power.

The trouble starts when you try to apply those predictions to treatment, not just research. Once a machine can detect autism earlier than doctors, a whole new set of practical and ethical problems emerge.

In order for a predictive tool to be useful for parents and patients, it would have to be accurate and relatively universal. That's certainly not true of this new model, its creators acknowledge. It will selectively predict certain kinds of autism: those that can be diagnosed by age 2 (some kids can't be positively diagnosed until much later), and types of autism that tend to run within families. For a predictive model to be useful for the general population, researchers would need to train their algorithm on a much, much bigger group. They could also improve accuracy by layering on other emerging predictive algorithms—ones based on genes, eye movements, and even baby babbling.

It would also need to be accessible to the right people. “Obviously we don't think that every kid in the population can get a scan at 6 months of age,” says Joseph Piven, the senior author on the UNC study—MRI is just too expensive and time-consuming. But genetic tests and family histories could help pediatricians hone in on high-risk infants and offer a scan to them.

If you figured out accuracy and distribution, then you'd need to provide effective treatment for those early-stage identifications. “I think we really do have to be thinking about these advanced computational methods for detecting autism—and what we're going to do once we detect,” says Zachary Warren, a clinical psychologist at Vanderbilt University who reviews autism therapies for the Agency for Healthcare Research and Quality. That doesn't mean diagnosis—at least not until the Diagnostic and Statistical Manual of Mental Disorders defines autism by something other than its behavioral markers. Tom Insel spent 13 years at the National Institutes of Mental Health trying to develop exactly that kind of quantitative framework for psychiatry—based on genetics, behavioral data, and physiological cues—and failed by his own account.