TMC PULSE

t m c » p u l s e | d e c 2 0 1 9/JA N 2 02 0 25 Artificial Intelligence will improve imaging-driven diagnoses and treatment B y C i n d y G e o r g e W hen the X-ray was discov- ered at the end of the 19th century, a new medical discipline was born. Radiology became a way to study, diagnose and treat disease. Today, expertise among radiolo- gists, radiation oncologists, nuclear medicine physicians, medical physi- cists and technicians includes many forms of medical imaging—from diagnostic and cancer imaging to mammography, radiation therapy, ultrasound, computed tomography (CT) and magnetic resonance imag- ing (MRI). As we move into the third decade of the 21st century, radiol- ogy—perhaps more than any other medical specialty—is poised for transformation. Thanks to artifi- cial intelligence (AI), radiologists foresee a future in which machines enhance patient outcomes and avoid misdiagnosis. "In the old days, X-rays were very shadowy, very difficult to interpret. They required a lot of expertise. Nowadays, with MRIs, you can see the anatomy really, really well. So now, the next step from that—which is a big jump—is artificial intelli- gence," said Eric Walser, M.D., chair- man of radiology at The University of Texas Medical Branch (UTMB) at Galveston. Early machine learning used information from a few cases to teach computers basic tasks, like identifying the human anatomy. Today, AI can distinguish patterns and irregularities in large collec- tions of data, which makes radiol- ogy an ideal application. Software can draw from millions of images and make diagnoses with speed and accuracy. "Ultimately, what you would want out of an AI algorithm is value," said Eric M. Rohren, M.D., Ph.D., professor and chair of radiol- ogy at Baylor College of Medicine and radiology service line chief for Baylor St. Luke's Medical Center. "It's pretty clear that the algorithms can do some pretty amazing things. They can detect abnormalities with a high degree of precision. They can see what, perhaps, the human eye cannot see. What's not known is: What is the value of that in a health care system? Are you truly improving patient outcomes and patient care by introducing a partic- ular algorithm into your practice? Does it have measurable impact in terms of better patient experience, hospital stay and better outcomes following surgery?" To that end, Baylor College of Medicine has created an internal library of all imaging data from the last decade. "It's being put into a research archive so that investigators can come in, pull that data, be able to research that data, develop their own algorithms and link it with the electronic medical records to see pathology and laboratory values and outcomes for those patients," Rohren said. "Computers, since they don't have the level of intuition that we have as humans, they really need to be trained in a systematic fashion off a data set that's been developed specifically for them to learn. That data set can be tens of thousands of examinations in order for the algorithm to be able to determine prospectively and predictively what it's going to do when it's faced with a scan it's never seen before." The American College of Radiology joined the AI revolu- tion by creating its Data Science Institute that aims to "develop an AI ecosystem beyond single institu- tions," according to the institute's 2019 annual report. Houston as a hub The region dubbed "Silicon Bayou" for its innovation ecosystem is becoming a hot spot for artificial intelligence ventures. Houston was ranked among the world's top large cities prepared for artificial intelligence, according to the Global Cities' AI Readiness Index released in September 2019. The city ranked No. 9 among places with metro area populations of 5 million to 10 million residents. The report was based on a sur- vey conducted by the Oliver Wyman Forum, part of the Oliver Wyman management consulting firm that tracks how well major cities are prepared to "adapt and thrive in the coming age of AI." ➟ AI Adds Big Data Power to Radiology

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