You’re a first-year medical student and Step 1 of three U.S. Medical Licensing Examinations (USMLE) looms. Study drills include everything from anatomy to physiology, aging to immunology. You pore over test prep books, boil down medical texts to memorizable flashcards, and scour an electronic library of an estimated 20,000 of cards assembled by other med students over more than five years, and analyze its annotated sample exam questions.
The new year dawns and with it comes the posting of the USMLE scores. You’ve earned a 272. You breathe a sigh of relief.
At the Department of Biomedical Informatics in the Blavatnik Institute at HMS, however, more studying is in order. Not by human students, but by a “proto-med student.”
“We’ve been providing the algorithm with information that is increasingly complex, much like what a first-year medical student would face,” says Andrew Beam, a research associate in the department and leader of the team that’s been developing the artificial neural network. “We started by providing it with the content from basic science textbooks, moved to scientific literature, worked to increase its high-level vocabulary, introduced more targeted material, then started providing it with actual test prep material.
“Like a medical student, artificial intelligence needs to begin with a broad foundation of knowledge so that it has a rough understanding of what the pieces are and a loose understanding of how they all fit together. If you just jump into the test prep material, everything looks random to the algorithm; it has no understanding of relationships. It has no context in which to make connections.”