Tag Archives: Enlitic

Signify Premium Insight: Enlitic Adopts Standard Approach in GE Partnership

This Insight is part of your subscription to Signify Premium Insights – Medical ImagingThis content is only available to individuals with an active account for this paid-for service and is the copyright of Signify Research. Content cannot be shared or distributed to non-subscribers or other third parties without express written consent from Signify ResearchTo view other recent Premium Insights that are part of the service please click here.

Imaging AI vendor Enlitic recently announced it has signed a deal with GE HealthCare, which will see the two firms integrate each other’s technologies.

The agreement means that GE will embed Enlitic’s platform into GE’s PACS software in order to help improve radiologist workflow and efficiency. GE’s adoption of the platform, dubbed Curie, seeks to improve data standardisation and increase efficiency by reducing the requirement for radiologists to spend time on tedious administrative tasks like correcting broken hanging protocols, for example.

While the partnership marks a significant milestone for Enlitic, it also represents a growing interest in AI tools being utilised for tasks other than image analysis.

The Signify View

As highlighted in numerous past Insights and evidenced by the challenges faced by vendors such as MaxQ AI, it can be difficult for a medical imaging AI vendor to survive from AI image analysis. Many markets are well served with lots of start-ups and scale-ups all targeting some of the most common use cases, while in other areas, market leaders have already emerged, making it difficult for smaller vendors to succeed.

Instead of trying to compete in these already well served markets, some vendors such as Enlitic are focusing on other areas of the medical imaging AI ecosystem. This wasn’t the original specialism of Enlitic, which previously focused on image analysis, with its latest funding round in 2019 taking the vendor’s total funding to $55m, a figure that was at the time very significant. But, the vendor was also working on a tool internally to standardise medical imaging data in order to make it easier for the developer to create algorithms.

This tool filled an uncatered for niche in medical imaging AI, and so was commercialised by Enlitic, becoming the company’s primary focus rather than the image analysis solutions it was originally developing.

GE’s decision to partner with Enlitic somewhat validates this decision, and, through the potential installed base and opportunities that GE offers, can help drive forward the commercialisation of the Curie platform. Moves such as this will be essential if Enlitic, and other vendors which offer AI tools which aren’t focused on image analysis, are to begin to generate significant revenues from such tools.

Where is the Value?

Such an ambition is attainable, but vendors such as Enlitic must highlight the value that their tools can bring. While the value of a product which automatically identifies pathologies on a medical image is self-evident, the utility of tools like Enlitic’s is harder to convey. The vendor must, for instance, illustrate the downstream benefit that can be gained from processes such as fixing hanging protocols and standardising nomenclature, all tasks which radiologists would have to complete manually.

Enlitic estimates that using AI to automate such tasks will save radiologists between 30 and 90 seconds per study, representing a significant improvement to efficiency. This hints at the opportunity GE’s partnership offers and explains the reason AI developers may look to focus on tasks before the reading of a medical image. However, the concept of leveraging AI to support workflow is not new per se, with many diagnostic viewers marketing “AI-enabled” support for workflow optimisation within the reading environment, achieved via self-development and partnerships with white-label applications.

For Enlitic, there is greater opportunity to have a more significant impact by addressing all elements of the reading process before the radiologist actually conducts the read, than by focusing on reducing the reading time for the radiologist. That, after all, is a small component of the whole care pathway. Moreover, it enables the radiologist to focus on high value tasks such as the diagnosis.

Although providers will likely consider the benefits of such tools to help with regards to efficiency, by reducing the need for radiologists to undertake tasks not directly related to the read, providers will also hope to improve patient outcomes. Time sensitive conditions, such as stroke or tension pneumothorax, for example could be treated more quickly, if radiologists can get to diagnosing more quickly rather than spending valuable time performing non-diagnostic tasks such as fixing broken hanging protocols.

Dealing with Data

The advantage of this standardisation becomes more significant in larger healthcare networks in which there is a greater range of disparate sources of data, sometimes with different naming conventions and varying protocols. As individual practitioners or departments send and request studies from across the network, the issues with inconsistencies are exacerbated. As different departments become more closely linked, these inconsistencies will have a more significant impact and the need for greater standardisation increases.

This is also a consideration for providers looking to leverage AI tools across hospital sites. AI tools which can improve standardisation of medical images and their associated metadata will facilitate the use of image analysis algorithms, helping to ensure that orchestration is conducted correctly, and the right scans are routed to the right places.

The question for GE is where this capability is layered into its PACS alongside GE’s own Edison Open AI Orchestrator. Does GE intend to use Curie to carry out the standardisation, before an image enters the AI platform and is then routed to the correct algorithm? Or is it layered in after GE’s own orchestrator? The latter may allow GE more specific control, but for Enlitic, the former would place it in a far stronger position – if its Curie platform was utilised between the PACS and AI platform its oversight of the flow of information and its role in standardisation is far more impactful, while also streamlining and support more effective use of AI tools within the orchestrator platform.

Making a Mark

Such opportunity means that transitioning to this or similar fields could be an attractive opportunity for other vendors. Many AI vendors that are presently associated with image analysis could be facing difficulty in the market and finding it hard to gain commercial traction. For these vendors, transitioning to standardisation and protocolling tools, could be a realistic alternative.

The same is also true for another less well-established vendors that are developing tools to facilitate AI development. Such vendors are focused on assembling datasets and creating toolkits or development “sandboxes” for vendors to utilise in the development of machine learning algorithms. These vendors may, quite naturally, pivot to standardisation platforms given that they have a repository of data as well as a significant amount of expertise. Offering such expertise up via partnerships, or even as the result of acquisitions could provide these typically niche vendors with an opportunity to gain greater commercial traction in a quickly consolidating market.

More broadly, such developments highlight the opportunity that AI offers away from imaging analysis. While that may be the most obvious use case for AI, there are a number of equally significant tasks that AI can be charged with accomplishing with fewer hurdles to commercialisation. Particularly in the near term such solutions will likely provide the bulk of commercial opportunities for vendors. Moreover, partners such as GE will also need to leverage this technology to improve their own portfolio offerings and ensure users have more timely and effective access to new AI-based tools, a clear area of growth opportunity. Further, as PACS and AI platforms blur, data standardisation and reading workflow performance will become a greater aspect of user decision making for purchasing PACS or sustaining existing installed base.

Enlitic is an accomplished vendor in this growing space with its $55m in funding, and the analysis algorithm, which has regulatory approval in Japan, evidencing its potential. However, the partnership with GE HealthCare for its Curie platform offers a potentially lucrative commercial route forward, opening one of the largest installed customer bases of imaging IT users worldwide, ultimately allowing the vendor a big opportunity to realise that potential.

About Signify Premium Insights

This Insight is part of your subscription to Signify Premium Insights – Medical Imaging. This content is only available to individuals with an active account for this paid-for service and is the copyright of Signify Research. Content cannot be shared or distributed to non-subscribers or other third parties without express written consent from Signify ResearchTo view other recent Premium Insights that are part of the service please click here