Tag Archives: Platform

Signify Premium Insight: A Platform on Which to Build

As the medical imaging AI market has matured, AI vendors are increasingly focusing on how best to navigate the last-mile challenges to deliver AI into provider workflows.  Vendors are adopting different strategies to achieve this aim, but one approach which has been increasingly considered and has grown more quickly than any other, is the use of orchestration platforms.

These platforms, which bring AI algorithms into hospital workflows by solving the challenges of back-end deployment, front-end integration, and algorithm orchestration, have proliferated in recent years. As such, there are almost 30 platforms available today. With that being the case, AI vendors must make increasingly difficult decisions about which platforms, if any, are worth pursuing. This is particularly true given the growing consolidation in the imaging AI market, a factor that could add a certain pressure to algorithm developer’s decisions

Signify Premium Insight: Bayer Acquires Blackford as Platforms Become Priority

Bayer, a vendor best known in radiology as a supplier of contrast agents, has revealed further intentions on medical imaging AI with the purchase of platform provider Blackford Analysis. The German firm’s announcement follows its entry into the AI platform space last year with the launch of Calantic, itself developed in conjunction with Blackford.

Bayer and Blackford have an established history together, with Blackford also being part of the larger vendor’s G4A Incubator programme in 2019. Despite this heritage, Bayer has said it will allow Blackford to continue to operate relatively independently, at ‘arm’s length’ from its parent company, while Bayer continues to offer its own Calantic platform.

Signify Premium Insight: Annalise.ai Enters into Nuanced Partnership

This Insight is part of your subscription to Signify Premium Insights – Medical Imaging. The content is only available to companies that have subscribed to this paid-for service. To view other recent Premium Insights that are part of the service please click here.

Medical imaging AI vendor Annalise.ai and Nuance Communications, a vendor which specialises in reporting and ambient clinical intelligence tools, have announced a partnership which will connect Annalise.ai’s diagnostic support solutions to more than 12,000 healthcare facilities currently on Nuance’s Precision Imaging Network globally.

With the agreement, Annalise hopes to gain exposure to a greater number of sites, allowing it to scale rapidly, while Nuance can utilise Annalise’s solution to enhance its growing Clinical Analytics Platform and complement its Natural Language Processing tools.

The Signify View

Medical imaging AI vendors are keen to extol the virtues of their partnerships. While these vendors are often quick to boast that their algorithms are being hosted by one of a growing number of AI platforms, the truth is that these platform providers are sometimes not very discerning. Some platform providers aim to simply give customers the broadest range of solutions possible. Sometimes these are bundled into clinical suites or workflow packages, but the breadth of solutions on offer is usually of paramount importance.

The approach of Nuance, bolstered by its recent acquisition by Microsoft, is subtly different. The partnerships it has fostered do help offer a range of capability to customers, but above that ambition, Nuance has been more discerning, only partnering with vendors which deliver solutions that offer providers significant clinical value. It is essentially only interested in collaborating with the vendors it deems the leaders in any product category. This marks a divergence from its original platform play, which took the form of a more conventional ‘marketplace’ approach aiming to offer a wide variety of tools to the end-user, but that platform, like many of the early marketplaces, failed to gain significant traction.

Annalise.ai, as well as Nuance’s other announced partners, Densitas and Perspectum, embody this ‘quality over quantity’ approach. In the case of Annalise, which can be regarded as a market leader given the sophistication of its comprehensive solution, the clinical value it has the potential to offer and the funding and clearances it has secured, the adoption of a comprehensive solution eschews the need for Nuance to adopt and integrate solutions from multiple providers for the same body area modality combination. Nuance’s orchestration capabilities mean that customers on its Precision Imaging Network can leverage Annalise’s strength to identify a multitude of findings, before findings are pushed to their reporting solution, ensuring they can more readily be utilised in clinical workflows.

Historic Improvements

In addition to this, however, Annalise.ai’s solutions could be used in synergy with Nuance’s strength in natural language processing (NLP). Nuance’s NLP could mine historic radiology reports to identify reports of interest. These reports could then be analysed by Annalise to identify incidental findings. While this would, in the first instance, enable providers to improve patient outcomes, it would also have broader implications, allowing the health of entire populations to be more effectively managed overall.

As well as having a presence in almost 80% of US hospitals (according to the vendor) Nuance’s network connects radiologists, providers, health-plans, self-insured employers, life sciences companies and other imaging stakeholders. The two vendors will hope that this breadth will enable such retrospective analytics to deliver value to providers beyond the clinician, and identify other areas where additional value can be delivered.

This highlights the difference between Annalise and Nuance’s collaboration, compared to other comparable partnerships. Where often vendors in partnerships essentially co-exist harmoniously, Nuance and Annalise hope to collaborate synergistically. Working together they hope to enhance the quality of reporting and efficiently enrich the quality of reports with data directly from the algorithms.

Regulation Restrictions

Wider trends in the medical imaging market also emphasise the potential offered by the partnership. Annalise has, as noted in past Insights, been progressing quickly in Australasia and Europe. However, its progress in the US has been stymied by the US-FDA’s reluctance to approve comprehensive solutions, treating the detection of an individual finding as though it were assessing a separate tool. Such an approach effectively prevents Annalise, which claims its CXR chest X-ray solution can identify 124 findings, from gaining regulatory approval in the US. Resultantly, Annalise has, been forced to break up its solution in a bid to secure approval for smaller subsets of the solution. Further, to accelerate the pace of crossing regulatory hurdles and forge an installed base in the US, the vendor has also been forced to settle for its tool’s use as a triage and notification solution, rather than one that can be used for diagnosis.

These barriers mean that Annalise would be facing a long, hard road to gain ground in the US, especially in the face of other vendors which have gained success with a single solution before expanding out to encompass increased clinical requirements. Partnering with Nuance, and gaining access to its vast installed base, immediately ameliorates that difficulty. The scale of Nuance, as well as its integration into providers’ workflows, means that for the time being, the lack of regulatory approval for detection won’t severely hinder Annalise, enabling it to be valuable as just a triage solution, albeit for a smaller number of its CXR solutions. Further, if the US-FDA does eventually rethink its approach to comprehensive solutions, it will be well placed to dramatically capitalise.

Even at present, though, both companies stand to benefit, while also granting their customers new opportunities. This is particularly true given that Nuance’s workflow integrations will help tackle another of the hurdles facing providers hoping to utilise AI for historic analysis; how to bring the analysis of historic data into current clinical workflows. Annalise needs to be able to access the data harboured by Nuance’s 12,000 care facilities, which depends on that data not only being made available, but also being formatted into a unified manner, where NLP and image analysis can be leveraged.

Patient Finding

The fruits of overcoming this challenging, in private markets at least, can be substantial. Providers connected to Nuance’s network who choose to use Annalise’s solution on their historic data could identify significant numbers of patients with incidental findings, missed findings or even misdiagnosis. In doing so, if these patients can be incorporated into hospital’s workflows, and assigned treatment pathways, they represent additional sources of revenue for providers. By utilising the collaboration between Nuance and Annalise, providers should be able identify patients that will benefit from interventions, which they themselves can charge for, while also improving outcomes for the patient.

Further, the purported access to data granted by the agreement with Nuance will also give Annalise another longer-term advantage, with the vendor being able to utilise the data as it continues to refine its algorithms and presumably expand into other clinical areas, as well as validating its solutions to increasingly convince providers and regulators alike of its merits.

Even with the apparent strengths offered by the partnership, there are several questions whose answers will be revealed over time. How invested in medical imaging is Microsoft and Nuance, for example? One of the motivations driving investment in medical imaging by cloud infrastructure providers is simply to sell more cloud services. This is likely one of the reasons for Microsoft’s acquisition of Nuance in the first place. The partnership with Annalise and other AI vendors will, if successful, aid in this regard, helping convince providers to transition to the cloud. However, Nuance’s heritage and strategy suggests this is not the sole motivation. Another question raised is why Annalise hasn’t developed its own platform? AI scale-ups offering their own platforms is fast becoming a developing trend, and Annalise are well placed to make such a move. However, the opportunity to scale with Nuance is too significant to ignore, especially in the US, and Annalise will hope to use it to “leapfrog” algorithm developers that natively developed platforms.

These are, however, relatively small matters in what is a grander ambition. The volume of platform launches throughout the year has increased dramatically, but against this backdrop, Nuance’s partnerships with Annalise, Densitas, and Perspectum have brought something different. Sophisticated AI solutions, AI orchestration expertise, a large global footprint of potential sites, backed by a global cloud technology behemoth with very deep pockets; a combination which could prove a recipe for success.

About Signify Premium Insights

This Insight is part of your subscription to Signify Premium Insights – Medical Imaging. The content is only available to companies that have subscribed to this paid-for service. To view other recent Premium Insights that are part of the service please click here

Signify Premium Insight: Tempus’ Arterys Acquisition and AI’s Turning Tide

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.

Medical imaging AI vendor Arterys recently announced it has been acquired by Tempus Labs (Tempus), in a move that ranks among the largest acquisitions of an AI vendor to date. The move continues AI’s march beyond its radiology bastion, with Tempus, backed by $1.3bn in VC funding, among the leaders in the precision medicine and treatment discovery segment.

The price Tempus has paid for Arterys – a vendor which has raised more than $70m in funding – or the exact nature of its plans for the company, have not been disclosed, although it is likely that Arterys will continue to offer its core radiology products, alongside supporting Tempus’ other operations in the preclinical and life science sectors.

The Signify View

Consolidation has appeared inevitable in the growing AI market for several years, however, until 2020, most acquisitions were small, as AI vendors sought to take advantage of competitors’ technology and add it to their own portfolios. Examples of such moves include Terarecon’s acquisition of McCoy Medical Technologies (for its Envoy AI platform) and Circle Cardiovascular Imaging’s acquisition of Corstem in 2019.

Latterly, however, acquisitions have begun to adopt a new timbre. Increasingly, when AI vendors have sought to incorporate a peer’s functionality into their own tools, they have turned to partnerships, rather than acquisitions, a far more flexible approach. In 2022, however, a new motivation for acquisitions grew. Companies whose core business is outside radiology, have more commonly looked to radiology AI companies to bolster their capability, with outpatient imaging Radnet acquiring Aidence and Quantib and nference acquiring Predible. Similarly some AI vendors themselves have pivoted towards lifesciences, a bid, in part, to avoid some of the challenges that the clinical use of AI presents.

Tempus’ acquisition of Arterys continues this trend. Tempus, with a heritage in life sciences, has now moved to capitalise on the success seen in radiology AI. In this regard, the precision medicine vendor has also been bold in its choice of target. At 11 years old, Arterys is one of the longer established medical imaging AI vendors in the space, and one of the first to receive US-FDA approval for a deep learning algorithm.

One Swallow Does Not a Summer Make

However, as seen in other instances, an effective algorithm alone does not necessarily translate into a successful business. Despite Arterys early success, it has also modified its strategy multiple times, transitioning from a cardiac algorithm developer, into a more generalist algorithm developer, and then more recently becoming one of the first independent algorithm developers to commercialise a native platform. Such pivots are driven by both ambition and necessity as medical imaging AI is, after all, a very competitive market, with many players competing for often-scarce dollars.

Amidst this competition, the acquisition will give reassurance to Arterys and ensure it can continue to offer its radiology products, while also benefitting from greater investment and allowing it to target a much more sizable life sciences opportunity as part of Tempus. Thanks to the funding, it will, in the near term at least, continue to be able to enhance its native algorithms, some of which are highly differentiated from those of their competitors such as its cardiac and neurological 4D flow solutions. It will also bolster the vendor in its efforts to become among the largest independent platform vendors, with the backing of its new owner giving it the war-chest to battle many of the segments’ other AI platform vendors.

Over the longer term, however, the boundaries between Arterys and Tempus are likely to be blurred. There will be less ability for the vendors to run as distinct entities and Arterys will be consumed by Tempus. As this happens, Tempus, leveraging the strengths of Arterys, will harbour a much stronger offering in life sciences and pharma, making strong headway on its precision medicine ambitions.

Using the acquisition’s medical imaging AI capability, Tempus will aim to enhance or even alter the diagnostic pathway, in a manner comparable to some of medical imaging AI’s most successful vendors.

Pharma Focus

Further to these designs on diagnostic pathways, Tempus is also set to use Arterys to redouble its efforts on the pharmaceutical space. Vendors have made commercial headway with some applications of medical imaging AI, with detect and triage applications gaining most success to date. Another use, quantification, has so far struggled to gain traction in radiology, and AI some vendors have instead focused on detect and triage capabilities.

Now, however, the opportunity for collaboration between Tempus and Arterys, and the combining of their portfolios, will enable the value propositions of both companies to be brought to the fore in pharmaceuticals, as well as clinical practice. As such, Tempus will continue towards its precision medicine ambitions, using Arterys’ capabilities to help develop an overarching platform that can leverage lots of sources of data, including real world data, to deliver the best personalised treatment for the patient.

These ambitions will, however, not be realised without first confronting come significant challenges. This will be particularly true for Arterys’ hopes in radiology. Other vendors have whole-heartedly focused on radiology, developed broader solutions which they have then pushed forward, fought to qualify for reimbursement, and have ultimately given providers a clear reason to adopt their solutions.

For Arterys, on the other hand, the motivations for adoption may not be so clear. While it does offer competitive solutions, unlike some of its most successful peers, it has not yet qualified for reimbursement in the US. What’s more, although platforms are one of the areas that have garnered the most interest in medical imaging AI of late, it is also a space that has become increasingly crowded. Given many of Arterys’ successes have come through third-party tools hosted on its platform, with only limited success from its natively developed solutions, it risks losing these third-party partners to other competitors, diminishing or at least undermining the value of its platform play.

Broader Approach

These challenges mean that the newly combined company may, over time, cease to focus so heavily on medical imaging AI. Any vendor that is to succeed in medical imaging must offer significant clinical value to providers. Tempus will hope to be able to offer this value, quickly scaling Arterys’ platform with an even broader range of algorithms.

Whether they choose to do this, however, is an entirely different question. So far, payoffs in life sciences have frequently dwarfed those in radiology AI, so Tempus may simply prioritise that market, letting Arterys’ traditional ambitions fall by the wayside.

Either way, for Arterys, the acquisition will be welcome news. The vendor has raised considerable funds, but, considering some of the extraordinary funding rounds, the qualification for reimbursement, and clinical guidance secured by some others in the medical imaging AI space, Arterys could start to look like an underdog. As it is, the acquisition by Tempus gives Arterys’ early investors a healthy exit, while also providing the vendor with the funds to fight battles in the radiology AI market and beyond.

There are other vendors that would also consider such an approach welcome news. Earning revenues to justify investors’ funding is proving harder for many vendors than they may originally have expected. Funding the clinical validation and sales activities that will enable them to target reimbursement and greater clinical adoption is tough, and unless vendors have the resource for such activities, they may increasingly struggle as their capital runs out.

Against such a backdrop, hindsight might make it clear that Arterys has been fortunate in its timing. Increasingly, larger companies, whether life science firms, modality vendors or even imaging IT firms might start to pick up successful AI vendors at ever more attractive prices as their funding dwindles. A daunting prospect for AI vendors, but perhaps the next step forward in the technology’s broader adoption.

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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

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