They interrupt blood flow around or inside of the brain, depriving it of oxygen, which is why timely treatment is critical. After three or four minutes, brain cells begin to die. Fortunately, artificial intelligence (AI) promises to drive progress on the diagnostic front. In a paper ("An explainable deep-learning algorithm for the detection of acute intracranial hemorrhages from small datasets") published in the journal Nature Biomedical Engineering last month, researchers at Massachusetts General Hospital in Boston describe a deep learning algorithm that can detect acute intracerebral hemorrhages, or ICHs, with a high degree of accuracy. Their work comes about a month after researchers at the University of California, Berkeley demonstrated an AI system that can predict Alzheimer's disease from brain scans up to six years in advance.

Publications like The Wall Street Journal, Forbes and Fortune have all called 2017 "The Year of AI." AI outperformed professional gamers and poker players in new realms. Access to deep learning education expanded through various online programs. The speech recognition accuracy record was broken multiple times, most recently by Microsoft. And research universities and organizations like Oxford, Massachusetts General Hospital and GE's Avitas Systems invested in deep learning supercomputers. These are a few of many milestones in 2017.

Publications like The Wall Street Journal, Forbes and Fortune have all called 2017 "The Year of AI." AI outperformed professional gamers and poker players in new realms. Access to deep learning education expanded through various online programs. The speech recognition accuracy record was broken multiple times, most recently by Microsoft. And research universities and organizations like Oxford, Massachusetts General Hospital and GE's Avitas Systems invested in deep learning supercomputers. These are a few of many milestones in 2017.

For this research, MGH surgeons identified seven distinct stages in a procedure for removing part of the stomach, and the researchers tagged the beginnings of each stage in eight laparoscopic videos. "We wanted to see how this system works for relatively small training sets," Rosman explains. "If you're in a specific hospital, and you're interested in a specific surgery type, or even more important, a specific variant of a surgery -- all the surgeries where this or that happened -- you may not have a lot of examples." In this case, the system had to learn to identify similarities between frames of video in separate laparoscopic feeds that denoted the same phases of a surgical procedure.