"Research Can Be First Step In Harnessing Power Of Artificial Intelligence To Interpret Medical Scans"

Researchers used machine learning techniques, including natural language processing algorithms, to identify clinical concepts in radiologist reports for CT scans, according to a study conducted at the Icahn School of Medicine at Mount Sinai and published in the journal Radiology. "The language used in radiology has a natural structure, which makes it amenable to machine learning," said senior author Eric Oermann, MD, instructor in the department of neurosurgery at the Icahn School of Medicine at Mount Sinai. "Machine learning models built upon massive radiological text datasets can facilitate the training of future artificial intelligence-based systems for analyzing radiological images." Study co-author, John Zech, a medical student at the Icahn School of Medicine at Mount Sinai said, "The ultimate goal is to create algorithms that help doctors accurately diagnose patients." Joshua Bederson, MD, professor and system chair for the department of neurosurgery at the Mount Sinai Health System and clinical director of the neurosurgery simulation core as well as the study co-author, explained, "Research like this turns big data into useful data and is the critical first step in harnessing the power of artificial intelligence to help patients."

• Eric Oermann, MD, Instructor, Neurosurgery, Icahn School of Medicine at Mount Sinai
• John Zech, Medical Student, Icahn School of Medicine at Mount Sinai
• Joshua Bederson, MD, Professor, System Chair, Neurosurgery, Mount Sinai Health System, Clinical Director, Neurosurgery Simulation Core

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