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Daniel Tse, a project manager at Google and an author of the journal article. In the new study, the researchers applied artificial intelligence to CT scans used to screen people for lung cancer, which caused , deaths in the United States last year, and 1.

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The scans are recommended for people at high risk because of a long history of smoking. Studies have found that screening can reduce the risk of dying from lung cancer. In addition to finding definite cancers, the scans can also identify spots that might later become cancer, so that radiologists can sort patients into risk groups and decide whether they need biopsies or more frequent follow-up scans to keep track of the suspect regions.

But the test has pitfalls: It can miss tumors, or mistake benign spots for malignancies and push patients into invasive, risky procedures like lung biopsies or surgery. And radiologists looking at the same scan may have different opinions about it. 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.

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Tse said. Tested against 6, cases with known diagnoses, the system was 94 percent accurate.

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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. Mozziyar Etemadi, a research assistant professor of anesthesiology at Northwestern University Feinberg School of Medicine, and an author of the study.

Topol was not involved in the study.

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Asked if artificial intelligence would put radiologists out of business, Dr. There are potential hazards, though. A radiologist who misreads a scan may harm one patient, but a flawed A.