Greg Freiherr, Industry Consultant
Greg Freiherr, Industry Consultant

Greg Freiherr has reported on developments in radiology since 1983. He runs the consulting service, The Freiherr Group.

Blog | Greg Freiherr, Industry Consultant | PACS| February 10, 2016

Watson, Come Here … Radiology Needs You

Watson, IBM, smart machines

Watson avatar courtesy of IBM

Fatigue. Bias. Trouble keeping up with peer-reviewed literature. These can spell trouble for radiologists. But machines are prone to none of them. Possibly best of all, artificially intelligent machines can fill in where needed … doing what has to be done, regardless of the circumstances.

IBM is counting on Watson Health to be one of those machines.

The company is building Watson Health, its artificial intelligence (AI) platform, to perform tasks that will benefit radiologists. Topping that list is summarizing the information in electronic medical records to provide the context that will help radiologists interpret images. A close second is an audit service that in one configuration looks for things that have been missed, in others expedites early interventions that can mitigate diseases that contribute to high mortality or cost. Cracking the top three on Watson’s list of possible applications is one that manages the radiologist’s worklist, providing radiological triage that moves cases up and down, depending on patient need. (A similar AI-enabled program, developed by vRAD, is nearing routine use. See “Will Artificial Intelligence Find a Home in PACS?”)

Ultimately these developers want to see Watson Health perform more sophisticated tasks, identifying cancers, circling suspicious masses and making the measurements that can help determine the effect of therapy or recurrence of disease.

The goal is to make the most of medical images and improve patient care, so as to help radiologists, not take their place, said Steven Tolle, an executive at IBM/Merge, the corporate conglomerate formed through the acquisition last fall of Merge Healthcare.

“Watson is not diagnosing the patient and certainly not replacing physicians,” said Tolle, chief strategy officer and president of iConnect Network Services.

While the search to image-enable the enterprise has helped many physicians by providing them with images that in the past had been only available to radiologists, not much has been done for radiologists. Watson’s EMR application would change that. Scheduled to be ready for use this year, it could find and summarize for radiologists information contained in the medical record, providing patient demographics, history and laboratory results. Watson Health might present these along with images from the patient’s prior exams.

IBM/Merge is not the first to work on a bridge from radiology to the EMR (see “Going Along to Get Along”). But Big Blue is the first to use AI in its construction.

Also scheduled for 2016 is a Watson Health “audit service,” Tolle said. This AI-enabled diagnostic workstation might help, for example, by prompting referring physicians to do follow-ups recommended by the radiologist.

“The radiologist might have read a chest CT, seen a nodule, and recommended follow-up in six months,” Tolle said. “We want to make sure that somebody schedules a follow-up exam of that patient.”

IBM plans to implement this audit service for high-priority diseases — top priorities are cardiovascular disease; cancers of the breast, skin, lung; and COPD— as well as diseases that impose high costs of care, such as diabetes and Alzheimer’s.

Watson Health will also provide technologies designed to make radiology more efficient and more effective. In the near future, Watson may juggle the order of cases on the radiologist worklist to give precedence to emergent needs. Brain bleeds would elbow their way to the top of the worklist, boosted there by a filter that recognizes patterns associated with intracranial hemorrhage.

To be of further help, Watson is being trained to provide access to peer-reviewed journals and medical texts relevant to the case being interpreted. Using pattern matching algorithms, similar to those used for facial recognition, Watson might look for images in a database that appear similar. These might serve as examples presented to the radiologist along with diagnostic “suggestions,” as Tolle calls them, generated by Watson.

“We want to provide options for the physician to consider,” he said.

Efficiency would be further improved, if Watson could learn to take over highly skilled but time-consuming tasks such as identifying and outlining masses, measuring them, and transposing the measurements into the radiology report. Initially Watson’s suggestions would be presented separately from the image being interpreted by the radiologist in another window or on a different monitor. The radiologist would always control what suggestions are accepted.

“Today the radiologist does this,” said Tolle, noting that in the future this might be delegated to Watson. “We are trying to streamline the radiologist’s workflow.”

Editor’s note: This is the second blog in a series of four by industry consultant Greg Freiherr on Working With Smart Machines. You can read the first blog, Will Artificial Intelligence Find a Home in PACS?, here.

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