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Lily Ge, a first-year medical student at , is making speedy progress on her lessons. When working to memorize details from her latest anatomy module, she flips through a virtual microscope that automatically pans to the organs she’s currently studying. To help wrap her mind around how different systems in the body interact and connect, she manipulates a virtual 3D body, zooming in to see “what loops behind what,” she says. If it seems as if the tools were tailored just for her, that’s because they are.

Ge’s class is the first to participate in a pioneering approach to called Precision Education, now under way at NYU Grossman School of Medicine. Partly funded by the American Medical Association, the initiative uses artificial intelligence (AI) to tailor curriculum and study aids to each student’s learning style and goals. “Unlike a one size-fits-all , Precision Education takes into account the complex mechanisms underlying each individual’s goals and needs in a way that can help improve learner outcomes and, by extension, patient outcomes,” explains , associate dean for educational informatics and the founding director of the .

On the surface, students may never notice the hand of AI. The system, which relies on a type of computational power called machine learning, works behind the scenes of the student portal. It crunches data on an individual’s academic record and their practical experience—patients seen, performance during procedures, professor feedback—to guide and course-correct throughout their academic journey. If a student is unsure of their specialty, for example, a tool for early career exploration taps predictive analytics to suggest electives. If a student regularly searches for a particular condition, an AI tool will surface additional videos, journal articles, and other information about it—the same way YouTube knows to serve cat lovers cute feline bloopers. Students can also mix and match how they absorb new information, so visual learners can read lectures, auditory learners can listen, and so on.

“Unlike a one size-fits-all curriculum, Precision Education takes into account the complex mechanisms underlying each individual’s goals and needs in a way that can help improve learner outcomes and, by extension, patient outcomes.”
—Marc M. Triola, MD, Associate Dean for Educational Informatics

For clinical experience, the algorithms draw upon de-identified patient data extracted through Epic, �ٺ���Ƶ Health’s electronic health records system, to help students hone their clinical decision-making skills. “The extensive dashboards and tools to see and understand these data are something that most other medical schools, trainees, and their coaches do not have access to,” says Dr. Triola. “The goal is to help students become the best doctors they can be and to make good choices about which electives and opportunities they want to explore on their journey to getting there,” he says.

As NYU Grossman School of Medicine completes its first year with the Precision Education tool kit, Dr. Triola and team are continually incorporating feedback from both students and educators to improve and expand the tools. The impact of Precision Education is expected to reach far beyond the walls of NYU Grossman School of Medicine, with other top medical schools seeking Dr. Triola’s expert counsel on how to implement similar tools into their own curriculum.

The next big step is bringing the AI-driven model to . �ٺ���Ƶ has already deployed an app called NoteSense, developed by , assistant dean for education in the clinical sciences and director of integrated clinical skills, which uses a type of AI called natural language processing to read treatment notes, provide feedback, and track improvement among residents. “Our AI learning tools are even more applicable to new doctors because, unlike a medical student who’s trying to learn the entirety of medicine, residents can focus in on a clinical specialty,” Dr. Triola says. “That can really help with the ‘precision’ of Precision Education.”