One of Henry Ford's most famous quotes is, "If I would have asked people what they wanted, they would have said a faster horse." As the story goes, he went on to create the Ford Motor company with the vision of having an automobile owned by every family in America. He then was able to mass produce automobiles at an affordable price while simultaneously being able to pay workers more money and creating disposable income for a new class of people. Diagnostic medicine is at the same turning point. Radiologists (as all physicians) are measured on production metrics called an RVU or a relative value unit.
He's sitting inside a dimly lit reading room, looking at digital images from the CT scan of a patient's chest, trying to figure out why he's short of breath. Health care companies like vRad, which has radiologists analyzing 7 million scans a year, provide data to partners that develop medical algorithms. Chief Medical Officer Eldad Elnekave says computers can detect diseases from images better than humans because they can multitask -- say, look for appendicitis while also checking for low bone density. Radiologist John Mongan is researching ways to use artificial intelligence in radiology.
These days, a radiologist at UCSF will go through anywhere from 20 to 100 scans a day, and each scan can have thousands of images to review. In health care, you could say radiologists have typically had a pretty sweet deal. They make, on average, around $400,000 a year -- nearly double what a family doctor makes -- and often have less grueling hours. "The biggest concern is that we could be replaced by machines," says Phelps Kelley, a fourth-year radiology fellow. He's sitting inside a dimly lit reading room, looking at digital images from the CT scan of a patient's chest, trying to figure out why he's short of breath.
In this special guest feature, Elad Walach, CEO of Aidoc, offers four important reasons today's budding radiologists need not fear AI displacement. Elad is the Founder and CEO of Aidoc, a smart radiology company that uses deep learning to streamline the workflow of radiologists. After earning his Master of Science in Computer Science from Tel Aviv University, he served as a Researcher and Team Leader in the Israeli Air Force, focusing on computer-vision and machine learning. His success in operational solutions and leadership has been pivotal in establishing Aidoc. He is a seasoned public speaker, appearing at various institutions including the Hebrew University of Jerusalem, Israel Society for Biological Psychiatry and Shaare Zedek Medical Center.
Artificial Intelligence (AI) has the capability to provide radiologists with tools to improve their productivity, decision making and effectiveness and will lead to quicker diagnosis and improved patient outcomes. It will initially deploy as a diverse collection of assistive tools to augment, quantify and stratify the information available to the diagnostician, and offer a major opportunity to enhance and augment the radiology reading. It will improve access to medical record information and give radiologists more time to think about what is going on with patients, diagnose more complex cases, collaborate with patient care teams, and perform more invasive procedures. Deep Learning algorithms in particular will form the foundation for decision and workflow support tools and diagnostic capabilities. Algorithms will provide software the ability to "learn" by example on how to execute a task, then automatically execute those tasks as well as interpret new data.