For years, one continuously swirling question in radiology has been whether artificial intelligence (AI) has become sophisticated enough to be used in clinical practice—and the most dreaded question of all: whether it is advanced enough to unseat the practicing provider. So far, the answer has been “not yet.”
And, for those waiting with bated breath, the answer is still no—and, it won’t be any time soon. But, according to many industry experts, there continues to be a great deal of ongoing work devoted to developing tools that can streamline and expedite the daily activities of the radiologist.
“The hype for artificial intelligence is far from what is actually being used as artificial intelligence,” says Alexander Logsdon, MD, an early interventional radiology resident at Nova Southeastern University.
But, while AI isn’t making clinical treatment decisions options, it is continuing to grow and develop, creating inroads into improving workflow efficiency and productivity. And, at some point, says Keith Dreyer, DO, PhD, vice chairman of radiology computing and information sciences at Massachusetts General Hospital, the industry will stop thinking of these tools specifically as AI.
“As the technology continues to advance, we’ll see things like we’re seeing today, such as early diagnostic solutions, to assist the radiologist. We’ll also see things that will enhance acquisition built into the scanners,” he says. “It will optimize workflow, productivity, and patient flow. After a while people will stop calling it AI and will think of it as simply something that makes their work better.”
Current AI Uses
Despite early predictions that AI solutions would eliminate the need for the radiologist, the uses and applications, to date, have been limited. The FDA has only approved 28 algorithms, but several offer the same solution, such as identifying suspicious pulmonary nodules or reducing CT dose.
In addition, Logsdon says, approved AI solutions also exist for identifying intracranial hemorrhage, spinal fractures, pulmonary embolism, pneumothorax, and rib fractures. Other abdominal algorithms awaiting approval focus on pinpointing free air, hemorrhage, dissection, and aneurysm.
Clinical implementation has been stymied, however, because these solutions aren’t readily generalizable throughout all facilities, says Mitchell Schnall, MD, chair of the radiology department at the Perelman School of Medicine at the University of Pennsylvania. Most AI tools, he says, are tested on small data sets that are largely unique to individual facilities.
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“Most AI solutions are not trained to work across data sets,” he says. “Most of the products we’ve tested can’t integrate and function in our system, and even those that do usually aren’t able to reach their claimed performance standards.”
This obstacle is difficult to overcome, he says, because many AI companies either don’t have access to large enough data sets to make their solutions feasible in most settings or they don’t have the bandwidth to test the products with enough clinical partners.
What Does AI’s Future Hold?
According to Dreyer, there’s significant room for growth in AI over the next decade.
“Over the next 10-15 years, I feel very confident that we’ll see algorithms that are in limited use today being deployed inside clinical practice for radiologists,” he says. “And, beyond that, we’ll see AI solving things that are difficult to solve today. “
These tools will be better able to completely segment organ systems on ultrasound, CT, and MRI, he says, as well as quantify lesions on previous and current scans, and predict morbidity and mortality from a series of X-rays or CT images.
Schnall also predicts improvements in reconstruction software that is currently designed to clean up images before the radiologist reviews them. There’s currently little confidence in these tools, he explains.
“I struggle with how much credibility to give these products because they’re really using data to estimate what typically is an artifact and what typically isn’t to eliminate what shouldn’t be there,” he says. “And, the entire focus of radiology is to pick up on the unusual.”