Case Studies

When it comes to teaching new dogs new tricks, radiology training programs need to be thinking about updating their curricula and preparing for both the short- and the long-term effects of AI and machine learning, according to “Toward Augmented Radiologists,” a new commentary published online in March in Academic Radiology.

Ever the visionary, Paul Chang sees AI as an asset to radiologists. As he sees it, “AI and deep learning doesn’t replace us. It frees us to do more valuable work.” The vice chair of radiology informatics at University of Chicago Medicine takes a quick look through the crystal ball at the four stand-out challenges facing radiology with the rise of AI.

To look into the future is to catch only a glimpse inside Simon Warfield’s radiology research lab at Boston Children’s Hospital. His team is pairing hyperfast imaging and deep learning to push the limits of medical imaging and artificial intelligence (AI) to identify, prevent and treat disease. He’s also eyeing ways AI will help as data sharing expands among research sites. “The research world needs to look forward to manage forward,” he says.

AI is hotter than hot in healthcare, according to AI market watcher CB Insights. Healthcare-AI funding reached $2.14 billion across 323 deals from 2012 through the second quarter of 2017—and has consistently been the top industry for AI deals.

(Spoiler alert: It’s a 69-page report that indicates the use of AI in healthcare is both promising and doable.)

When it comes to AI and machine learning, the regulatory trail has been blazed and the approval gates through open. The FDA has approved a couple dozen apps over the last year and a half—and the momentum is clearly building with Scott Gottlieb at the agency’s helm and recent moves to ramp up staffing to meet the demand.  

Lawrence Tanenbaum is a big believer in AI, as a tool to create better images, offer a more comprehensive view of a patient and more effectively handle imaging’s increasing volume and complexity. Bigger yet, AI is the impetus to change the way radiology and medicine are practiced across the care spectrum.

The power of artificial intelligence (AI) is enabling clinical breakthroughs that identify biomarkers without invasive procedures, diagnose skin cancer with a photograph, predict adverse clinical events, and recommend treatments based on current literature. Getting these innovations to market requires access to large, complex data sets to train the AI models.

Healthcare is in an intense era of retooling, similar to the Industrial Revolution of several centuries ago. Bright minds in healthcare and technology are shaping new tools powered and empowered by artificial intelligence, machine learning and deep learning. This burgeoning Age of Intelligence is matching minds and machines to sharpen knowledge and insight to improve the delivery of care for patients, populations, practitioners and providers. 

Artificial intelligence (AI) is rewiring the way we think about healthcare. And rewiring the way doctors predict, diagnose and treat disease, how exams are carried out and how health systems are run. Is AI a game-changer? Absolutely, and the game is changing a lot faster than many think.

West Feliciana Hospital (WFH) has been serving patients in the small town of St. Francisville, Louisiana, since 1970, but its imaging capabilities were limited for a long time. As a result, the hospital gained a bit of a reputation among referring physicians in the area—when in doubt, they would just avoid WFH altogether and send patients more than 30 miles away to Baton Rouge.

The Miami Cancer Institute (MCI) started nearly 10 years ago as a collection of widely distributed service lines without an identifiable physical presence. The idea was to bring together local and regional cancer experts from every medical discipline and every support service. The vision was mass collaboration around providing world-class cancer care to patients from across the Southeastern U.S. as well as Latin America and the Caribbean.