The week before heading off to college, Harry Emerson was diagnosed with type 1 diabetes. Without the ability to produce insulin, the hormone that transports blood sugar to fuel other cells, he’d need help from medical devices to survive, his doctors told him. Eager to get on with school, Emerson rushed through the process of familiarizing himself with the technology, then went off to university.

Because people with type 1 diabetes make very little or no insulin on their own, they need to keep careful track of their blood sugar as it changes throughout the day. They inject insulin when their blood sugar is too high or when it’s about to spike after a meal and keep fast-acting carbs ready to eat when it dips too low. The mental math can be dizzying. “Every time I eat, I have to make a decision,” Emerson says. “So many subtle factors have minuscule effects that add up, and it’s impossible to consider them all.”

For many, tracking this data means finger pricks, manually logging the results from their blood glucose monitor every few hours, and injecting insulin accordingly. But those privileged enough to access state-of-the-art devices can outsource some of their decision-making to machines. Continuous glucose monitors, or CGMs, measure blood sugar every few minutes via a tiny sensor under the skin, sending readings to a pocket-sized monitor or smartphone. Insulin pumps, tucked in a pocket or clipped on a waistband, release a steady stream throughout the day and extra doses around mealtimes. If the CGM can talk to the insulin pump in what’s called a “closed-loop” system, it can adjust doses to keep blood sugar within a target range, similar to the way a thermostat heats or cools a room.

These control algorithms work, but they rely on hard-coded rules that make devices inflexible and reactive. And even the fanciest systems can’t get around life’s imperfections. Just as a phone’s fitness app can’t track steps you take when you’re phoneless, a CGM can’t send data if you forget to bring your monitor with you. Anyone who’s tracked macros knows how tricky it is to accurately count carbs. And for many, eating three predictably timed meals a day feels about as realistic as going to bed at the same time every night.

Now a PhD student at the University of Bristol’s Department of Engineering Mathematics, Emerson studies how machine learning can help people live with type 1 diabetes—without thinking about it too hard. In a June study published in the Journal of Biomedical Informatics, Emerson collaborated with the University Hospital Southampton to teach a machine learning algorithm to keep virtual diabetes patients alive. The team trained the AI on data from seven months in the lives of 30 simulated patients, and it learned how much insulin to deliver in a variety of real-life scenarios. It was able to figure out a dosing strategy on par with commercial controllers, yet it needed only two months of training data to do so—less than a tenth required by previously tested algorithms.

To Emerson, machine learning algorithms present an intriguing alternative to conventional systems because they evolve. “Current control algorithms are rigidly defined and derived from lengthy periods of patient observation,” he says, adding that this training is also costly. “It’s not necessarily practical to keep going about it that way.”