The researchers thought computers might do better. They created a neural network, with multiple layers of processing, and trained it by giving it many CT scans from patients whose diagnoses were known: Some had lung cancer, some did not and some had nodules that later turned cancerous.

Then, they began to test its diagnostic skill.

“The whole experimentation process is like a student in school,” Dr. Tse said. “We’re using a large data set for training, giving it lessons and pop quizzes so it can begin to learn for itself what is cancer, and what will or will not be cancer in the future. We gave it a final exam on data it’s never seen after we spent a lot of time training, and the result we saw on final exam — it got an A.”

Tested against 6,716 cases with known diagnoses, the system was 94 percent accurate. Pitted against six expert radiologists, when no prior scan was available, the deep learning model beat the doctors: It had fewer false positives and false negatives. When an earlier scan was available, the system and the doctors were neck and neck.

The ability to process vast amounts of data may make it possible for artificial intelligence to recognize subtle patterns that humans simply cannot see.

“It may start out as something we can’t see, but that may open up new lines of inquiry,” said Dr. Mozziyar Etemadi, a research assistant professor of anesthesiology at Northwestern University Feinberg School of Medicine, and an author of the study.

Dr. Eric Topol, director of the Scripps Research Translational Institute in La Jolla, Calif., who has written extensively about artificial intelligence in medicine, said, “I’m pretty confident that what they’ve found is going to be useful, but it’s got to be proven.” Dr. Topol was not involved in the study.

Given the high rate of false positives and false negatives on the lung scans as currently performed, he said, “Lung CT for smokers, it’s so bad that it’s hard to make it worse.”