With a deepening focus on unleashing novel applications of artificial intelligence (AI) across--and beyond--neurosurgery, a multidisciplinary team of physicians and mathematicians are collaborating on advanced approaches to diagnosis and patient care, developing data-driven methods that hold potential for progress across the continuum of medicine. Investigations into clinical applications for AI, with a focus on neurosurgical care, have gained significant momentum with the recruitment of Eric K. Oermann, MD, assistant professor in the Departments of Neurosurgery and Radiology and a leading expert in AI applications in medicine. Dr. Oermann brings deep expertise at the intersection of neurosurgery and mathematics to research projects that apply data science and algorithms to answer pressing neurosurgical questions as well as those that apply to medicine far beyond neurosurgery. "Neurosurgery tends to be the technical spearhead of the broader medical world, innovating to benefit our own patients and medicine with a capital M," he says. "So our discoveries in AI are at the next forefront of technological innovation in medicine, writ large." Dr. Oermann developed the vision for his research in close partnership with Daniel A. Orringer, MD, associate professor in the Departments of Neurosurgery and Pathology.

Scientists are developing a multitude of artificial intelligence algorithms to help radiologists, like this one that lights up likely pneumonia in the lungs. Artificial intelligence (AI) is poised to upend the practice of medicine, boosting the efficiency and accuracy of diagnosis in specialties that rely on images, such as radiology and pathology. But as the technology gallops ahead, experts are grappling with its potential downsides. "Just working with the technology, I see lots of ways it can fail," says Albert Hsiao, a radiologist at the University of California, San Diego, who has developed algorithms for reading cardiac images and improving their quality. One major concern: Most AI software is designed and tested in one hospital, and it risks faltering when transferred to another.

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.

Researchers from the Icahn School of Medicine at Mount Sinai have leveraged natural language processing algorithms to automatically identify clinical concepts in radiologist reports for computed tomography scans. Using more than 96,000 radiologist reports associated with head CT scans performed at The Mount Sinai Hospital and Mount Sinai Queens, researchers trained the computer software to understand text reports written by radiologists, achieving an accuracy of 91 percent. The NLP algorithms were used to teach the computer clusters of phrases, including words such as phospholipid, heartburn and colonoscopy. Results of the study were published this week in the journal Radiology. "The language used in radiology has a natural structure, which makes it amenable to machine learning," says senior author Eric Oermann, MD, an instructor in the Department of Neurosurgery at the Icahn School of Medicine.