Pranav Rajpurkar

Pranav Rajpurkar, 28, has developed a way for AI to teach itself to accurately interpret medical images without any help from humans.

His systems can already perform at the level of human experts, flagging pathologies that might otherwise be missed and preventing unnecessary medical procedures due to false positives. Rajpurkar’s newest model, called CheXzero, could improve their performance further and expand the types of images they can handle.

When Rajpurkar introduced an early model allowing computers to read chest x-rays in 2018, there was a problem: a shortage of data. At the time, he and others in the field relied on radiologists to manually label images that AI systems used for learning. Since it takes a few minutes for a person to label a single image, and AI systems require hundreds of thousands of images to understand what they’re looking at, the field soon hit a roadblock.

Rajpurkar’s new approach skips the human labelers altogether by comparing a set of medical images—taken from any number of private or public data sets—with the radiology reports that almost always accompany them. The system can automatically match the images to issues the reports identify in writing. This means that CheXzero can use massive databases to learn to spot potential problems without human input to prepare the data first—a technique known as “self-supervision.”

Rajpurkar, who is an assistant professor of biomedical informatics at Harvard Medical School, says his dream is to eventually build a system capable of ingesting a patient’s medical records and then identifying problems doctors may have missed.