Computer vision is designed to recognize and understand images and data to execute actions that only humans were once thought to be capable of performing. The healthcare industry has already seen a bunch of benefits coming from the rise of Artificial Intelligence (AI) solutions. Computer vision technology is highly contributing to the mechanism, which can potentially support many different applications delivering life-saving functionalities for patients. The emerging field of computer vision focuses on training computers to replicate human sight and understand the objects in front of them. Big players like Amazon and Facebook are already in the market, investing millions of dollars in the technology.

Solving a challenge: This was the first task set out by the Mount Sinai AI Consortium, a group of scientists, physicians and researchers at New York City–based Mount Sinai Health System dedicated to developing artificial intelligence in medicine. "We wanted to [apply AI] in the healthcare context and tackle a problem that is clinically impactful and relevant to our practices," says Eric Karl Oermann, instructor in the department of neurosurgery at the Icahn School of Medicine and director of the AI program, dubbed AISINAI. The challenge the group landed on was to identify markers of acute neurological illnesses, such as hemorrhages and strokes. Time matters because a patient's "clinical condition is something that worsens, in some cases, by the minute," says Oermann. "They're extremely time-sensitive." With this in mind, the group set out to see if they could find a way to use AI and deep learning to save some of those precious minutes.

"With a total processing and interpretation time of 1.2 seconds, such a triage system can alert physicians to a critical finding that may otherwise remain in a queue for minutes to hours," says senior author Eric Oermann, MD, Instructor in the Department of Neurosurgery at the Icahn School of Medicine at Mount Sinai. "We're executing on the vision to develop artificial intelligence in medicine that will solve clinical problems and improve patient care." This is the first study to utilize artificial intelligence for detecting a wide range of acute neurologic events and to demonstrate a direct clinical application. Researchers used 37,236 head CT scans to train a deep neural network to identify whether an image contained critical or non-critical findings. The platform was then tested in a blinded, randomized controlled trial in a simulated clinical environment where it triaged head CT scans based on severity.