Survey: Majority of Imaging Leaders See Important Role for Machine Learning in Radiology

Feb. 1, 2018
While there is much hype around machine learning and its uses in healthcare, a recent survey indicates that machine learning is not just a buzzword, as 84 percent of medical imaging professionals view the technology as being either important or extremely important in medical imaging.

While there is much hype around machine learning and its uses in healthcare, a recent survey indicates that machine learning is not just a buzzword, as 84 percent of medical imaging professionals view the technology as being either important or extremely important in medical imaging. What’s more, about 20 percent of medical imaging professionals say they have already adopted machine learning, and about one-third say they will adopt it by 2020.

A recent study by Reaction Data sought to examine the hype around artificial intelligence and machine learning, specifically in the area of radiology and imaging, to uncover where AI might be more useful and applicable and in what areas medical imaging professionals are looking to utilize machine learning.

As Healthcare Informatics Editor-in-Chief Mark Hagland noted back in November, at RSNA 2017 the most prevalent topic was machine learning and how much of an impact it will have on the practice of medicine and on the business of healthcare overall.

Reaction Data, a market research firm based in American Fork, Utah, got feedback from 133 imaging professionals, including directors of radiology, radiologists, imaging directors, radiology managers, chief of radiology and PACS administrators, to gauge the industry on machine learning.

The vast majority of respondents, 84 percent, view machine learning as being either important or extremely important in medical imaging, and 25 percent view it as extremely important. Of interest, practicing radiologists are the more skeptical of machine learning’s applicability and usability, with department leadership holding a more optimistic opinion of this new technology, according to the report.

The survey also found that the vast majority of imaging professionals are somewhat familiar with machine learning, with only 9 percent citing that they are “extremely familiar” and another 9 percent saying that they are not familiar with the technology.

Chiefs of radiology think they are the most familiar with machine learning, however, other titles that are a lot less familiar with the technology think it is more important. For instance, about 90 percent of imaging directors say that machine learning is very important, but about 40 percent also say they are not familiar with the technology.

“This indicates we may be dealing with a mild case of COMO (fear of missing out). They responded that they aren’t that familiar with machine learning, but they feel it’s incredibly important. They catch wind of all the buzz, and all of a sudden you have something important,” the report authors wrote.

The study also looks at the adoption rate for machine learning. Seven percent of respondents said they have just adopted some machine learning and 11 percent say they plan on adopting the technology in the next 12 months. Fourteen percent of respondents said their organizations have been using machine learning for a while. About a quarter of respondents say they plan to adopt machine learning by 2020, and another 25 percent said they are three or more years away from adopting it. Only 16 percent of medical imaging professionals say they have no plans to adopt machine learning.

Among those respondents who said they will never utilize machine learning, close to half (46 percent) said they are unsure of AI’s usefulness right now, and 39 percent said their organizations aren’t “forward thinking.” Fifteen percent said humans are better than AI.

Interesting to note, the study found that there has been very little adoption by imaging centers, and all of the current adopters are hospitals. Fifteen percent hospital respondents said they have been using machine learning for a while and eight said of hospitals said they have just adopted the technology.

For those respondents who have adopted machine learning, 25 percent use Hologic as their vendor, and 17 percent use GE Healthcare. Looking at other vendors being used, 13 percent use Google for machine learning, and another 13 percent use iCAD. Philips Healthcare and Nvidia are used by 8 percent of respondents and Zebra Medical Vision and Arterys were both cited by 4 percent of medical imaging professionals.

Looking at mindshare among providers, 15 percent of respondents cited IBM as a company that offers machine learning and 10 percent cited Google. Other companies cited by respondents as companies that offer machine learning include Zebra, Siemens, GE Healthcare, Philips, Apple, Terrarecon and HealthMyne.

The most common application for machine learning is in breast imaging, cited by 68 percent of medical imaging professionals as a current or potential use case for the technology. Therefore, it comes as no surprise that many of the top vendors currently being used are also long-standing industry leaders in breast imaging modalities and software solutions, according to the report.

When asked what do they use or plan to use machine learning for, radiologists noted many areas where they plan to apply machine learning algorithms—61 percent cited lung imaging, 58 percent cited chest X-rays, 38 percent cited bone imaging, 36 percent cited cardiovascular imaging, 34 percent mentioned liver imaging; 26 percent cited neural aneurysm as a use case and 16 percent cited pulmonary hypertension as a use case.

Drilling down into those respondents who currently use machine learning, three-fourths use the technology for breast imaging. The next most-used application cited by respondents who have adopted and use machine learning was lung imaging (25 percent), which indicates how far ahead breast imaging is compared to other scan types. Among those respondents who plan to use machine learning, 66 percent cited lung imaging, followed by chest X-rays (63 percent), 62 percent cited breast imaging and 41 percent cited bone imaging as a potential use case.

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