Tag Archives: medical imaging

Signify Premium Insight: Amazon in Prime Position with HealthLake Plans

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.

Last week Amazon made clear its intentions in medical imaging, announcing two new capabilities in HealthLake focused on medical imaging and analytics.

The Seattle-based tech firm says that the abundance of data created in medical imaging is slowing down decision-making in hospitals. In response, the cloud vendor has launched Amazon HealthLake Imaging, which is designed to expedite medical imaging retrieval in clinical workflows, as well as powering existing medical viewers and analysis applications. This, the vendor claims, can result in considerable cost savings.

However, with Microsoft’s Nuanced-derived healthcare expertise, Google’s recent moves into medical imaging, and Oracle’s inherited incumbency via its Cerner acquisition, has Amazon done enough to win the interest of providers?

The Signify View

Tech giants’ interest in healthcare is nothing new. Amazon, like other Nasdaq darlings, has made various approaches to different healthcare markets over recent years, from the launch, and subsequent shuttering of Amazon Care, a primary care service, to its Amazon Pharmacy play. Recently however, several of the world’s biggest tech firms have redoubled their focus, setting medical imaging firmly in their sights. After Microsoft’s acquisition of Nuance, which closed in April 2022 and Google’s recent launch of its Medical Imaging Suite, Amazon has become the latest tech firm to make a concerted imaging effort.

Like Google’s launch before it, the launch of Amazon AWS’ HealthLake Imaging suite is not festooned with brand-new, never-before-seen capability. Instead, many of the tools and partnerships included in the package have been available previously in various guises. However, the new packaging highlights Amazon’s increasing focus on selling cloud services to acute and outpatient providers as interest in, and understanding of the technology increases. While many of the tools have been previously available, it would likely have taken an already knowledgeable user at a provider to capitalise and work out how best the range of tools offered by Amazon could be applied to their own imaging departments. The packaging and positioning of Amazon’s latest effort, however, should help providers more clearly appraise the potential of cloud adoption for their imaging departments, easing the transition for more mainstream providers.

Such positioning, however, is only enough to make AWS more accessible. What Amazon hopes will encourage providers to commit, is its boasts about price. In its blog detailing the new solution, Amazon estimates that HealthLake Imaging helps reduce the total cost of imaging storage by as much as 40%.

The Cost of Delivery

This figure, as is often the case with those used in marketing materials, should be taken with a pinch of salt. No doubt Google, Microsoft and other cloud providers harbour some technologies which are also designed to help reduce the cost of storing images on the cloud. However, the fact that Amazon has publicly stated the savings that providers can expect indicates the vendor’s confidence in its ability to offer providers an affordable option.

Cloud adoption, can, after all be stymied by the cost, or at least perceived cost, of making the transition. While this is less of an issue for flagship academic providers and the premium they are willing to pay to have the latest and most experimental technology, for the acute and outpatient providers, cost is a far greater barrier. If Amazon is to truly capitalise on the revenue-making potential that cloud provision in medical imaging offers, however, this mass market is ultimately where the vendor must target.

By highlighting the cost-savings providers can expect to make if they adopted Amazon’s imaging cloud solution, even if the actual savings delivered are not quite at the quoted 40%, the vendor hopes to overcome the perception that cloud is prohibitively expensive, and at least engage mainstream providers in a conversation.

Even with such savings, cloud could still prove too expensive, depending on the volume, complexity and standards of the data held by the provider, but, crucially, these factors stem from providers’ individual circumstance. Moreover, the shift to cloud for imaging can also require substantial investment in network infrastructure (e.g. local bandwidth) to leverage the benefit of cloud-based performance.  While there will be providers for whom AWS’ HealthLake Imaging product is still too expensive, the advertised and expected cost savings, will likely be enough to convince some providers, particularly when other factors, such as cybersecurity or the requirement to deliver capability across complex outpatient networks, for example, are considered.

Choosing Between Sellers

At present, the key differentiators between cloud providers are still minimal. While different providers may have different strengths, individual niches where they excel and particular partnerships that will ease certain use cases, any of the major cloud providers can, in essence, offer almost the same broad capability in cloud services for imaging. However, despite this comparability, leading cloud vendors are still beginning to better arm themselves and shape their identities in an attempt to build links to certain customer bases. Amazon’s focus on efficiency and the cost savings it offers is one such strategy, a play that, as highlighted, stands to place cloud capability firmly in the reach of acute and outpatient providers.

Other cloud providers also have their own strengths, however. Microsoft’s Azure finds itself in a particularly strong position, largely thanks to its acquisition of Nuance. Most obviously, that acquisition gives Microsoft a direct line to a claimed 77% of hospitals in the United States. However, that acquisition also fits in with Microsoft’s broader portfolio. It is, after all, not difficult to see the possible synergies with Nuance’s Powerscribe solution (and nascent, yet impressive DAX ambient reporting), combined with Microsoft’s ubiquitous tools, including Teams videoconferencing. This could bring ambient listening to all consultations and telehealth visits, leaving essentially every interaction structured and stored on the cloud along with relevant medical images.

Google, meanwhile, may lack the Nuance play that Microsoft can lay claim to, and it may lack the relentless operational focus that Amazon has developed through its commerce heritage, but its expertise in search, AI and broader image analysis, will give its own strengths, making it, for example, an attractive provider for leading academics focused on using their data libraries to develop their own AI algorithms.

Expected Arrival

In most cases though, these are concerns for the future. At present many providers aren’t considering long-term population health-focused imaging data repositories in the cloud, or developing their own AI tools. Instead, most providers are looking to the cloud for improved accessibility, efficiency, security and cost.

With these basics amply covered by all leading cloud providers, at present, which cloud provider hospitals choose is likely to depend more on customer-context, rather than unique capabilities. It doesn’t necessarily matter, for example, if a radiology department harbours a desire for an AWS imaging IT platform deployment if it is part of a large hospital network, which has just agreed an enterprise-wide deal with Azure. Almost all leading Imaging IT software vendors have some degree of flexibility on cloud-provider for hosting their applications, making cloud adoption often an enterprise, as opposed to departmental, decision.

By a similar token, hospitals in regions where there are restrictions on public cloud provision, where there is a preferred partner or a requirement for in-region datacentres, for example, have needs that trump any smaller local preference for individual cloud providers.

Despite these considerations, there is one area where AWS might have an advantage. AWS has arguably worked its way into a broader group of informatics partners (and larger market share) as “preferred” cloud provider, than some of its chief competitors. While some providers will disregard the partnerships their IT vendors have fostered, for many, simply adopting their imaging IT vendor’s preferred cloud provider partner will prove to be the most straightforward route to transition to the cloud, and as such, all else being equal, will be the one that is chosen.

There are some factors that will become increasingly important over time, such as the ability to manage and retrieve unstructured data, the ability to offer analytics so providers can use their cloud resource most efficiently, and even the adoption and ingestion of different data standards from across an enterprise imaging platform. However, in the near term, such subtleties are far from a provider’s priority.

In the near-term one of the main priorities, particularly for many mainstream providers, is cost. As such, Amazon’s claims of cost savings along with its repackaged and repositioned offering may make it an obvious choice for some. And for now, when fresh, first-time opportunities abound, that should be enough for the Seattle-based tech giant to deliver.

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: Risk and Reward – The Maturation of Medical Imaging AI

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.

Earlier this month, Viz.ai announced that it had received US-FDA clearance for its automated right ventricle/left ventricle (RV/LV) ratio algorithm, a new component of the vendor’s Pulmonary Embolism (PE) solution. The RV/LV algorithm will enable the automated assessment of potential right ventricle dilation and therefore help to identify right ventricular dysfunction, before delivering the results quickly to the entire care team using Viz’s PE solution.

The move represents the latest FDA clearance for Viz.ai, as it continues to grow its care coordination platform and expand beyond its original stroke care remit. The move also highlights a growing trend in medical imaging AI of vendors expanding product portfolios beyond a single use case, and also beyond image analysis.

The Signify View

As medical imaging AI vendors have matured and proved themselves worthy of increasingly lofty funding rounds, companies are having to expand beyond their original briefs to continue to provide value to the doctors that use them. Some of the most successful vendors have sought to offer this increased value by adding additional capabilities along the care pathway, beyond the slice of the workflow devoted to image analysis itself.

In the case of Viz.ai, this originally meant expanding into elements of stroke care such as triage and decision support, with the vendor’s care coordination platform aiming to expedite the treatment of the most urgent cases. Latterly, instead of expanding along the care pathway, vendors have been looking to leverage their expertise more broadly, with Viz, expanding into other vascular conditions.

For Viz, and other vendors, the key to adoption isn’t just about  the detection algorithms themselves. While their effectiveness is important, slight variances in specificity and sensitivity between vendors won’t make or break a provider’s decision to go ahead and make a purchase –  instead the value comes from the care coordination platform and the value that an AI developer can offer across the whole workflow. This is key as they translate their expertise into other areas. There may be niche vendors with slightly more performant algorithms in certain specific tasks, but these vendors will not be able to match the value brought about by a complete care coordination platform.

There are risks to this approach, however. Viz.ai, and other peers adopting a similar strategy such as Aidoc, and some Chinese vendors have been able to raise considerable amounts of money by advancing into new clinical areas and broadening their product portfolio. While such moves give them a head start over some more specialist vendors, they may also risk spreading themselves too thinly, stymieing their ability to fully deliver on their promises in the areas they first gained success.

Better Together?

Some vendors are forging partnerships to mitigate this exposure. Aidoc, for example, has chosen to add quantification capabilities to both its stroke care and pulmonary embolism solutions by looking externally. Aidoc’s own detect and triage capabilities are bolstered by a perfusion solution from I cometrix for stroke, and RV/LV solution from Imbio for its pulmonary embolism solution. This has allowed Aidoc to strengthen its care coordination platform, bringing quantification and stratification tools to market, while its partner gains access to many of Aidoc’s sites, giving the vendor significant potential upsell opportunities.

Unlike Aidoc, Viz developed the entirety of its stroke care platform in-house. However, for its pulmonary embolism solution, it also turned to a partner, forging links with Avicenna.ai to deliver the detect and triage capabilities for pulmonary embolism. While such a move will see the vendor relinquish some control, partnership offers a significantly expedited rollout. Rather than starting from scratch, having to develop a solution and conduct clinical validation studies over multiple years, a timespan that could result in the vendor losing ground to competitors.

Adopting such a strategy also requires Viz to further develop a back-end architecture for the native and partner algorithms to work seamlessly together, a move which could see the vendor follow in the footsteps of Aidoc and herald the commercial launch of an integrated AI platform.

The Importance of Being Useful

Regardless of the specifics surrounding vendors’ expansions into other clinical areas, be it Viz or any other AI vendor, the approach of leveraging triage and stratification tools is significant. For instance, it highlights that instead of being content with offering tools only useful for image analysis in other clinical areas, developing fully fledged care coordination platforms to serve other clinical situations is now a clear priority. Whether the actual image analysis part of that solution is developed internally, or offered via a partnership is fast becoming immaterial, as the real value of such solutions doesn’t stem from image analysis itself. Instead, in many cases, providers will benefit from leading AI vendors’ abilities to bring imaging analysis algorithms into a considered workflow, to increase their utility.

Some tools, also confer other advantages. Triage tools for example, have a simpler regulatory pathway than CADe or CADx image analysis algorithms, which, are seen to harbour more potential for patient harm. This can offer vendors a more efficient route to market. While the products they will be able to sell as a result of the approval may be more limited compared to solutions cleared for diagnostic use, such clearances will at least enable vendors to begin generating revenue and launch commercially in new markets, offering them a foundation to build on.

More broadly the expansion of some of medical imaging AI’s largest vendors into wider clinical areas, seeing them apply their expertise into more diverse use cases represents the growing maturation of medical imaging AI vendors.

Remember the Objectives

The ultimate aim of medical imaging AI is not to shave seconds of the read time of a chest X-ray, for example or even identify the presence of an indicator of a clinical condition. It is, above all else intended to improve patient outcomes; a final result that is based on the totality of a patient’s care, along their entire care journey.

The portion of this journey that actually entails the analysis of medical images is small. As such, although image analysis is the use case for AI that is discussed most excitedly, there are opportunities elsewhere along the care pathway that can have a more substantial impact on patients’ eventual outcomes. The addition of risk stratification tools such as the RV/LV algorithm from Viz epitomises this.

The vendor’s USP has long been to apply its expertise beyond the image analysis portion of the workflow with its care coordination platform. Not only does this deliver the assistance to identify findings from medical images, but it also helps imaging departments, and other departments more broadly, to better manage patient care and make interventions earlier. Compared to the relatively slight impact that shaving a few seconds off a read time can have for a provider, even for high read volume applications, the use of AI in this broader way can be far more significant.

Further, this offers a more sophisticated method of identifying the leaders in the medical imaging AI market compared with simply looking at which vendor has the greatest number of FDA cleared algorithms, or which has been able to raise the most capital. Instead, it is increasingly possible to assess vendors based on how sophisticated their tools are, and how much value they can offer providers. There is no single, solitary route to adding this value, with comprehensive solutions, and some sophisticated point solutions, which alter the diagnostic pathway, also offering broader value to providers alongside some vendors’ expansion into additional clinical areas, and along care pathways (end-to-end solutions as previously termed by Signify Research).

In this regard, broader imaging IT vendors have an advantage. With large installed bases and their presence across radiology departments and beyond, these vendors, with the right tools, could alleviate many of the bottlenecks faced by providers. However, at present these vendors aren’t aggressively leveraging this advantage, leaving the likes of Viz and its peers to make the early headway.

Whether they are able to capitalise long-term remains to be seen, but for now at least, moves such as that made by Viz, and some of its peers, show the maturation of medical imaging AI away from a “one-trick” image analysis focus toward impactful care outcomes.

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

Signify Premium Insight: Screening Marks the Next Step in Intelerad’s Grand Ambition

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.

Intelerad added another company to its growing portfolio in August, acquiring breast and lung screening specialist PenRad Technologies. The acquisition of the vendor, which provides software to enhance the efficiency of screening programmes, is the latest in a string of purchases including Ambra Health, Insignia and Lumedx among others.

The deal, which was of an undisclosed value, will bolster Intelerad’s offerings for mammography and lung workflow tools and analytics. Its three core products, PenRad for breast imaging, PenLung from lung screening, and PenTrac for patient tracking and reporting will, according to Intelerad, help radiologists using its enterprise imaging platform to optimise workflows and manage screening programmes more efficiently.

The Signify View

Since Hg Capital became the majority investor in Intelerad in 2020, the vendor’s plans have, over time, become clear. Intelerad has set its sights on building out a fully-fledged enterprise imaging offering, a system that will cater to the needs of most providers and offer a genuine alternative to both other specialist imaging IT vendors as well as the solutions offered by larger international medical imaging vendors.

The acquisition will help in this regard. The integration of screening tools into the core imaging IT platform, while not unique, does add additional competency that will allow the firm to compete against some of the largest imaging IT providers. Furthermore, it will also help differentiate Intelerad from its current peer group of small and mid-size imaging IT “challenger” vendors. Such differentiation will also be particularly attractive to the outpatient sites that presently form the majority of Intelerad’s customer base.

Screening tools are also likely to increase in importance over time. Screening programmes in the US are well established for mammography, but are growing in sophistication, with increased uptake of more advanced modalities such as 3D mammography, ABUS and MRI, as well as additional reporting on harmonised diagnostic metrics such as risk scores and breast density. Another factor that could also mean providers are more willing to invest in tools such as those offered by PenRad are changes to reimbursement rates. Lower rates of reimbursement for screening mammography will increase the importance of screening efficiency. For screening providers, which rely on high volumes to drive revenues, any tools that enable greater reporting automation and more women to be screened, could therefore be very valuable.

In the US, Lung screening on the other hand is still underutilised, with patients often choosing not to participate in screening programmes when they are available. Despite this, the uptake of screening is still being encouraged, with, for example changes to screening rules making more people eligible to participate. Moreover, given the nascency of lung screening so far, PenRad’s lung assets will further differentiate the Intelerad offering.

Timing is Everything

The timing of the acquisition also makes sense. Following on from the Covid 19 pandemic, there has been a greater emphasis of care in outpatient settings. This is a market that Intelerad can serve effectively, although, without PenRad it lacks some of the specialist tools to be able to effectively capitalise on the requirements of screening providers. There are, for example, specialist workflow elements, registry integration requirements, and AI integrations which, among other needs, are distinct enough from typical radiology use cases to necessitate the acquisition if Intelerad is to succeed in the screening market.

While many PACS vendors offer some screening capability, these complexities mean that many informatics vendors also lack the specialist screening capability that Intelerad has acquired through its purchase of PenRad. In many cases, PACS vendors will “white-label” tools from vendors such as PenRad, or work with breast modality workstation vendors such as Hologic. Developing this capability in-house is not impossible for a vendor such as Intelerad, but it would have taken time to get right. By buying PenRad, Intelerad gets this capability right away, while also preventing any of its competitors picking up the firm.

The deal also makes sense for PenRad. While its tools are valuable, as a specialist company it is at risk of enterprise imaging vendors, whether smaller specialists or larger, broader imaging vendors and growing breast AI specialists increasingly encroaching on its turf as they too look to capitalise on the resilient screening market.

Acquisitive Ambitions

Despite the potential that the acquisition of PenRad offers in the outpatient and screening space, the capability it brings is not transformative for Intelerad, nor will it likely mark the end of the vendor’s acquisitive streak. Intelerad is, after all, focused on assembling a complete enterprise imaging solution, and, as long as Hg Capital is willing to support the vendor, Intelerad will look to make deals for the remaining gaps in its capability.

One such opening would be for digital pathology capability. While such tools are not yet necessitated by providers, they are, as discussed in a previous Premium Insight, increasingly looking for their vendors to be able to offer a plan which allows them to take advantage of digital pathology when they choose to. This requirement has led several imaging IT vendors to ensure they can meet this need, with Sectra and Philips offering the capability in-house, while the likes of Siemens Healthineers and Fujifilm have chosen to partner with Proscia and Inspirata respectively to offer the capability. Not to be outdone, Intelerad could choose to pick up a company focused on digital pathology and integrate it with its broader imaging IT offering. However, given the nascency of digital pathology, particularly in Intelerad’s key market of the US, partnering might represent a viable near to mid-term alternative as it has for Fujifilm and Siemens. Partnership is also an option that Intelerad is also willing to take, as illustrated by its partnership with Blackford Analysis for AI platform capability, for example.

Another area arguably more deserving of Intelerad’s focus is Advanced Visualisation (AV). Not only does this absence represent a gap compared to most of its peers, being able to offer AV capability would give the vendor a better chance of sealing deals at acute sites and helping the company expand beyond its core outpatient customer base. There are opportunities for Intelerad to offer white label solutions to customers, however, this route is more limited in terms of customisation and does not offer the same long-term certainty as offering solutions developed in house. Taking such an approach also means that Intelerad would not be able to keep all the revenues from the solution, a factor which could impede its ability to innovate in the future. Solving the “AV” challenge is not urgent, but as AV is increasingly de-coupling from modalities sales channels and gradually overlapping with AI image analysis tools, the firm will not want to wait too long without a clear plan of how to address the AV gap in its offering.

Acute Solutions

There are other capabilities that are also increasingly important, particularly if Intelerad has designs on the acute space. Many deals are now agreed on a long-term basis and include professional service and consulting elements. At present, Intelerad could miss out on some major contracts given that it is not among the vendors best placed to deliver on these requirements at scale and on multiple geographic fronts, a headache that has also challenged fast-growing peer Sectra. Other elements also important given growing pressures on imaging services are fleet management and operational analytics tools. Not only would such tools stand Intelerad in better stead as it competes for major deals with acute providers, but these allow the vendor to use them as a foundation to increasingly engage in lucrative service activities, helping providers realises their performance targets and meet their KPIs for example.

While adding these and other capabilities would not be cheap, if Intelerad were willing to invest the time and resource in fully integrating them, it would emerge with one of the most complete enterprise imaging solutions available. This in itself is no guarantee of long-term success; the imaging IT market moves slowly, and organic growth is hard to come by. As such, further acquisitions may be necessary for Intelerad to continue to gain market share and the revenues that it brings. However, the investment made by Hg Capital, along with additional investment from TA Associates, suggests that the vendor is not finished, and that more acquisitions are likely on the way.

The more pertinent question is how long HG continues to back Intelerad. It has proved its commitment in the near term, but at some point, it is likely to want to exit and realise a return. When this is, and how advanced the integration of its acquisitions is at this point is something that remains to be seen.

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

Signify Premium Insight: The Tightrope Upon Which Lunit Must Walk

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.

Lunit recently announced that it has received preliminary approval for an IPO on the South Korean KOSDAQ index. Following the approval, the Seoul-based AI developer now plans to submit a listing within the first half of the year.

The outfit already has partnerships with several large international imaging vendors, including GE Healthcare, Philips and Fujifilm to incorporate its AI capability into their imaging systems, but Lunit says the money from the funding round will enable it to further develop its AI product range, and expand its global commercial reach. This will entail promoting its products, which include an AI tool for analysing mammograms, an AI solution for analysis of tissue slides for cancer biomarkers, and a comprehensive AI solution for chest X-rays that detects 10 of the most common findings, in more markets across the world.

The Signify View

That Lunit has decided to place its fortunes in the hands of public investors should come as no surprise. As detailed in a recent Premium Insight discussing medical imaging AI vendors with more than $100m of venture funding, Lunit, like many of its successful peers shares some common traits. The vendor has, for instance, taken a very robust approach to product development. Instead of relying solely on one of the available training datasets, which can introduce some ambiguity and aren’t always perfectly labeled, Lunit has chosen to use training data validated against biopsy results. This helps ground the AI tool’s algorithm and minimise the likelihood of errors. A tool is only as good as the data it is fed so supplementing images with other clinical data is a prudent approach.

In a similar vein, the vendor has also been thorough with regard to clinical validation. One of the hurdles stymieing the broader adoption of AI is a lack of robust evidence. To be profitable AI vendors need to prove their solutions are valuable to providers, to warrant hospital budgets and convince policymakers that their tools deserve reimbursement. To do this these vendors must undertake strenuous, detailed clinical validation studies. These are expensive and time consuming to conduct, but they are necessary. This need is compounded as AI vendors look to grow globally, with developers obliged to prove their solutions are as effective on non-local populations. Lunit has been able to meet this requirement, and can point to a wealth of published studies open to scrutiny, as well as regulatory approvals for its Insight CXR and Insight MMG chest X-ray and mammography solutions in the USA, Europe, Japan and South Korea and for SCOPE PD-L1 in Europe.

Available Options

In addition to this technical capability, the vendor has also been commercially savvy. As well as selling its products directly, Lunit has made its products available on several AI marketplaces, allowing providers which use, for example, Sectra’s imaging IT solutions to incorporate its tools through that company’s Amplifier Marketplace.

More significantly, the vendor has also sought to utilise partnerships to target new markets. It has received $26m from liquid biopsy specialist Guardant Health. As well as boosting Lunit’s bank balance, the collaboration will help the AI vendor target the US oncology market with its SCOPE tissue analysis platform. Lunit has also looked to international imaging vendors to grow, with the company inking deals with GE Healthcare, Philips and Fujifilm helping encourage Lunit’s adoption among those vendors’ install bases.

Such endeavours are for naught, if the products themselves are inadequate. Lunit, however, has avoided this trap. While its range of products is small, comprising of three solutions, one for chest X-ray, a second for mammography and a third for pathological tissue analysis, they are focused on valuable areas. Insight CXR, for example is a solution that is on the rising tide of increasingly comprehensive AI tools which, like those from Annalise AI and Oxipit AI seek to provide greater clinical value to doctors than narrow AI solutions which are often more limited. Similarly, Lunit’s SCOPE solution is set to benefit from the growth of digital pathology and the establishment of closer ties between diagnosis and treatment.

A Time for Temerity?

Despite these strengths, however, listing publicly also represents a risk for the vendor. Reports suggest the listing offers Lunit a valuation of around $500m. This is a far cry from the valuations of the likes of HeartFlow and Viz.ai, which saw valuations as high as $2.4bn and $1.2bn respectively in their recent fundraising endeavours, however it is still a significant sum for a vendor that, according to Signify Research’s AI in Medical Imaging report, achieved just over $1 million in revenue and a loss of $16.5 million in 2020. While the IPO will furnish the vendor with ample capital, it also adds considerable pressure. It risks facing many private investors necessitating consistently strong quarterly performances, rather than sympathetic private investors au fait with the medical imaging AI market, which are likely to be understanding of more modest returns in the name of sustainable long-term progress.

There are other hurdles too. The enormous valuations that some medical imaging AI vendors have been able to achieve through the high availability of funding for AI firms over recent years, have, in some instances proved a hindrance to these AI firms when they have looked to list. The likes of HeartFlow and Keya Medical both sought to go public, before being forced to postpone their plans, in part due to the inability of public investors to match these lofty valuations. Furthermore, in many instances, vendors that did go on and list publicly have seen their share prices fall as they were unable to meet the high expectations of their public investors. This has been true of all Lunit’s closest South Korean peers.

Vuno listed at an initial price of KRW32,150 in February 2021, and is now trading at around KRW10,500. DeepNoid shares initially traded at a price of KRW25,200 in August 2021 but now sit languishing at an all time low of KRW11,400. JLK, meanwhile went public in December 2019 with an initial price of KRW8,330, rallied to KRW14,150 in September 2020 but now trades at KRW6,220. The market as a whole has suffered, with the KOSDAQ falling 13% year-to-date, but JLK, DeepNoid and Vuno have all underperformed even relative to this benchmark, falling 20%, 38% and 44% respectively.

A Hard Market

Making headway amidst these underperforming peers will prove difficult. This challenge will also be compounded by market complexities facing its products. While the tools themselves are sound, they are competing in difficult markets. Mammography for instance has well-established incumbent vendors such as Hologic and iCad, as well as a plethora of breast imaging AI start-ups each trying to eke out a share of the market. Similarly, Lunit’s Insight CXR product will not only face stiff competition, but even when used it will be fighting for a small percentage of the limited reimbursement available for chest X-rays. Both could be successful products, but could be slow to return sizable revenues. Lunit SCOPE is likely to be similar. There are considerable opportunities pertaining to digital pathology AI, clinically as well as in other areas such as drug discovery. However, the digital pathology market is so nascent, it is unlikely to significantly contribute to Lunit’s bottom line for several years.

These weaknesses do point to one area in which Lunit should prioritise following its IPO. As well as commercialisation efforts, building sales and support networks in new markets, Lunit must also spend heavily on the development of new tools. The vendor must channel its expertise into targeting new areas that offer high potential returns, whether for better-reimbursed modalities, high-value use cases, or care coordination tools that expand beyond image analysis itself, Lunit needs to supplement its current strength with tools that will be lucrative into the long term.

Don’t Look Down

Ultimately, Lunit’s growth up to this point has positioned it in a difficult spot. While the vendor must move forward, doing so requires overcoming considerable adversity. This is not a problem that is unique to Lunit, but each medical imaging AI vendor that makes it to this pivotal moment, must navigate it in its own way. Balancing the needs of its investors while continuing to invest in product development and market creation, will be tough. The need to create significant revenues and profits to justify its lofty valuation, while not neglecting the robust way it has approached algorithm training and clinical validation, which helped establish its credentials, means walking a tightrope, where one slip can send a share price tumbling.

Lunit will see a future in which the company is successful. Consistently generating revenues, with sustainable growth, a secure user base and an innovative roadmap. The vendor can lead its investors to this future, but its challenge will be in ensuring they don’t lose faith and fall along the way.

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 

Terarecon logo on silicon chip

Signify Premium Insight: The Concerted Effort that TeraRecon Must Make

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.

Last month saw Boston-based vendor ConcertAI secure $150m in series C funding, adding to the $150m series B round in 2020 and boosting the company’s valuation to $1.9bn. The AI start-up, which specialises in offering real-world data to life sciences firms to manage regulatory clearances and develop clinical trials, and supporting healthcare providers to improve patient experience, will use the money to scale its software solutions.

The funding puts ConcertAI in a strong position. But will AV and medical imaging AI firm TeraRecon, which was integrated into ConcertAI in November 2021, also see any benefit?

The Signify View

TeraRecon first became linked to ConcertAI after being acquired by corporate parent SymphonyAI in March 2020. This portfolio of companies seeking to develop new generation AI solutions across a variety of sectors, from retail to financial services, brought TeraRecon on board and placed it alongside stablemate ConcertAI. At the time the deal seemed to offer clear synergistic opportunities for both parties such combining population data from ConcertAI with imaging biomarker technology and expertise to improve patient stratification for oncology clinical trials. Alternatively, providers could utilise imaging data from TeraRecon alongside EHRs and real-world data from ConcertAI to develop more integrated care management solutions.

Since then, however, TeraRecon has been relatively muted. Instead of revealing new, integrated products, public releases and announcements have slowed and several key c-suite personnel have left. This seems something of a regression from several years ago when TeraRecon was among the earliest of AI vendors to promote the AI-marketplace-platform model, securing patents and forging ahead with a novel approach to radiology AI’s last-mile challenges. Moreover, the senior leadership at TeraRecon were at the forefront of every industry debate and event discussing the role of AI in medical imaging.  Concurrently, the vendor was among the strongest independent AV vendors, securing good market share and beginning to make connections between AV and AI.

The firm hasn’t given up much ground from this position in AV, a market that moves slowly after all, but neither has it moved forward whilst other vendors have made headway, somewhat negating its early ascent. One initiative, for example, was to integrate AI tools from its platform with its own AV capabilities, packaging the combinations into specialist “premium” suites that were more attractive than individual tools. While TeraRecon’s progress slowed on this front, imaging IT vendors, AV vendors and modality vendors have incorporated competitive AI offerings, increasingly eroding the specialism that allowed TeraRecon to shine.

Move to the Money

Against this backdrop of increased competition and lengthy implementation cycles for clinical AI, integrating TeraRecon into ConcertAI is a sound move that offers a more direct route to financial success. Making significant returns in clinical AI is a difficult and drawn-out affair. The technology’s profitability is stymied by several barriers including a lack of reimbursement and a lack of financial impact studies, as well as the fundamental question of who will foot the bill. In preclinical and life sciences, on the other hand, the route to returns is much clearer, with pharmaceutical firms willing to invest in specific drug discovery projects, effectively using ConcertAI or alternatives as an external research team with project or milestone-based fee structures. This approach enables ConcertAI to gain commercial traction whilst waiting for the clinical market to mature. In the near term this could leave TeraRecon as a diagnostic imaging specialist whose expertise is applied to the preclinical space, with areas such as companion diagnostics a potential strength. For ConcertAI, having such expertise in imaging analysis in-house, and promising to utilise imaging data alongside other clinical data, could be a major selling point, improving the vendor’s odds of courting big-pharma and top academic provider interest.

Despite that, in the grand scheme of ConcertAI’s opportunities, TeraRecon’s existing AV business does not appear to be a  priority. ConcertAI has recently announced strategic agreements with the likes of Pfizer and Bristol Myers Squibb, so the returns of its funding round will be spent on the development of capability and service that can support such multi-billion-dollar companies. TeraRecon is not a central part of that strategy. It will, for the most part, be able to maintain the share it has carved out for itself within the AV IT market mid-term, and its technology will lend an edge to ConcertAI in preclinical, but it is unlikely to be able to chart its own course, and invest in its own growth in AV, as it would have been able to prior to its acquisition.

A Deal to be Done?

Given this impasse at which TeraRecon sits, it could be seen as an attractive acquisition target by vendors looking to round out their imaging IT portfolio. While ConcertAI will value TeraRecon’s AI capabilities and the vendor’s AV expertise, aside from being a dependable, albeit comparatively small source of revenue, it will be a lower priority to the vendor. As such ConcertAI could look to pare of TeraRecon’s AI abilities to bolster its preclinical and life sciences package, and then sell off the remaining AV business.

There are several vendors that would both benefit from such an acquisition and have deep enough pockets to make it a reality. Two of the most obvious names are Intelerad and IBM Watson Health. Since private equity investor HG Capital acquired a majority stake in Intelerad in early 2020, the imaging IT vendor has been on an acquisition spree, picking up Ambra, Digisonics and LumedX among others.

The vendor has also shown that it is beginning to link together the capabilities of these formerly disparate businesses into one cohesive whole (see In Step with the HIMSS Set, Intelerad Marches Forward). However, this enterprise imaging platform to-be, as yet lacks an AV solution, an omission that could be readily addressed by the acquisition of TeraRecon. What’s more such a deal would also net the vendor TeraRecon’s 10% share of the North American AV IT market in 2021, handily propelling Intelerad’s total imaging IT market share from 3.5% to 5% in North America.  The story is similar for IBM Watson Health. Freed from the wider tech business Watson Health’s new owners, Francisco Partners, could handily add TeraRecon’s AV capability and market share, to advance Watson Health’s along its enterprise imaging journey.

The Here and Now

For ConcertAI, the funding is another sign of confidence in strategic focus on real world evidence for life sciences. With a valuation of $1.9bn, it is clear great things are expected of the start-up. These, in the near term at least, are unlikely to come from TeraRecon and its strengths in image analysis or AV capability.

There is an advantage to using image analysis in its preclinical and drug discovery remit. Longer term there are lucrative possibilities such as the identification and cataloguing of imaging biomarkers, enabling diseases to be increasingly diagnosed from imaging alone, reducing the need for biopsies and other interventional diagnostic procedures. Such tools could be commercially successful, but would first require significant investment in research and development and would still take several years for any sizable returns.

Instead, it seems that TeraRecon, and the capabilities it brings, may not be the best complement to ConcertAI’s trajectory, while the company’s recent quietude and personnel changes also suggest change could be afoot.

Ultimately, regardless of its origins, this change could be welcome, with the clinical markets in which TeraRecon blossomed, increasingly under pressure; AV tools are being incorporated into broader imaging IT platform vendors to enhance diagnostic capability, interest is growing in edge AI and modality vendors are looking to bring such technology to their hardware, and there is growing appetite for the care pathway approach. Competition from leading imaging giants such as Siemens Healthineers, Philips and GE Healthcare is only going to intensify as AV is encompassed into broader diagnostic care packages of modality, edge AI, diagnostic viewer and service line offerings, while emerging AI platforms such as Blackford Analysis, Aidoc and others attempt to carve out their own piece of the imaging analysis market.

ConcertAI’s funding round will not solve these problems for TeraRecon. However, as ConcertAI grows its path will increasingly diverge from TeraRecon’s AV heartland, forcing the latter to act. Whether that is as part of a different parent, or in new partnerships with others, change is essential. For TeraRecon, stasis is unsustainable.


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