Artificial Intelligence in Imaging 2020
Shifting Mindshare

† This definition comes from KLAS’ 2019 Healthcare AI report.

State of the Market: How Have Provider Organizations’ Plans Changed since 2018?

AI in Imaging Has Accelerated

In 2018, many interviewed organizations felt more time and research were needed to identify use cases and prove an ROI for AI in imaging. Now, nearly half of interviewed organizations are live with or, more commonly, piloting an imaging AI program (30% are piloting; 16% are live). Those in pilots report use cases related to nonspecific algorithms that enhance workflows and help physicians diagnose conditions. Those who are live report multiple use cases, including natural language processing of radiology/clinical notes and post-processing of images to make it easier for radiologists to diagnose.

Deployment Is Closer Than Ever

Organizations are closer than ever to deploying AI in imaging. In 2018, over one-third of interviewed organizations with AI plans reported being at least three years out from deployment. That percentage has been cut in half, with no organizations reporting in 2019 that they are more than five years away. Those with the furthest timelines report hesitation caused by an influx of AI technology that lacks a proven use case. Even with this uncertainty, many organizations mention a willingness to partner with vendors and pilot new technology.

Depth of Adoption Expected to Increase

Organizations today expect deeper AI adoption (e.g., more use cases and more imaging departments) than did organizations interviewed in 2018. One contributing factor is less expected pushback from physicians. A radiology VP explained, “A year or two ago, . . . my radiologists and cardiologists thought that AI signified doomsday. Today, those same physicians are asking when they can get their hands on AI. They see the value that AI can bring to them and their patients.”

IBM Watson Health Leads in Mentions; Considerations of Other Vendors Increase

In 2018, IBM Watson Health was mentioned three times more than any other vendor when report participants were asked which vendor is best positioned to deliver AI in imaging. While IBM Watson Health still receives the most mentions, their total mindshare has decreased by almost half due to increased interest in a variety of other vendors. Some respondents mention IBM Watson Health’s head start in AI in general, and indeed the vendor’s general AI offerings have a larger customer base than their imaging AI offerings. Referring specifically to these imaging offerings, respondents (who may or may not be actual customers) report tempered confidence, noting that despite the vendor’s large pool of data and resources, development has been slow.

Aidoc and Nuance have seen the biggest increases in mindshare, though the vendors differ widely. With multiple solutions that already have FDA clearance, Aidoc is described as being ahead of the game and as having a fast-moving, targeted approach. Nuance customers have not yet seen concrete AI deliverables, but respondents feel the vendor’s existing speech recognition customer base may give Nuance an advantage in imaging AI.

Several respondents also feel that provider organizations and niche/start-up vendors will be well positioned to facilitate AI success in imaging. Provider organizations—specifically large organizations and academic health centers—are seen as having the experience to know how AI can benefit patients and the data resources needed to make it successful. Though not specified by name, niche/start-up vendors were noted to be flooding the AI market and are seen as innovative but potentially lacking long-term viability.

Zebra Medical Vision Inspires Confidence While Gaining Visibility; GE Healthcare and Nuance Also Grow in Mindshare

After IBM Watson Health, the next most frequently mentioned vendors were Zebra Medical Vision, GE Healthcare, and Nuance. Zebra Medical Vision is described by respondents as having a strong understanding of imaging and as being neutral, nimble, and willing to partner with other vendors. Many also mention that several Zebra solutions already have FDA approval. GE Healthcare and Nuance are seen as well positioned to deliver AI in imaging based on their existing installs of other technology. Respondents highlight GE Healthcare’s access to capital and data and mention the vendor’s AI efforts related to imaging modalities (e.g., flagging potentially critical images); a couple report concerns about GE (one based on previous experiences and one related to cost). Respondents who mentioned Nuance are optimistic about the early information and promises Nuance has shared. 

iSchemaView was mentioned by just three respondents but receives the highest average confidence rating. All three respondents report excitement about the vendor’s RAPID technology and its ability to help providers deliver faster, more precise care to stroke patients.

Other Mentioned Vendors

The following vendors were mentioned by one or two respondents each. They may offer AI technology only through partnerships with other vendors or they may not currently have an AI offering.

Provider Perspectives on Top-of-Mind Vendors

Includes only vendors with three or more mentions; vendors ordered alphabetically

Given the uncharted territory of the AI market, provider organizations place great weight on the references of their colleagues when making technology purchase decisions. Below are early insights from respondents regarding the top-mentioned vendors for imaging AI. These insights represent general market perceptions of the vendors, not feedback from actual clients.

Note: A historical look at several vendors’ general ability to deliver new technology can be found in KLAS’ 2018 Imaging AI report.
‡ Respondents were asked to name the vendors best positioned to deliver AI in imaging and to rate their confidence in the vendors’ ability to deliver.

How Healthcare Organizations and Technology Vendors Can Drive Success with AI

As reported by some of the healthcare industry’s most successful AI users

KLAS’ other two recent reports on healthcare AI (a 2018 report on AI in imaging and a 2019 report on AI in healthcare in general) offer several best practice suggestions healthcare organizations and vendors should consider for the deployment and adoption of AI.

† Additional information on partnership ratings for some vendors can be found in KLAS' 2018 imaging AI report.