Tag Archives: Partnership

Signify Premium Insight: GE’s Delicate Dance at the Chinese Communist Party

GE HealthCare has recently announced that it is planning to form a new joint venture with the China National Medical Device Corporation. The Chinese National Medical Device Corporation is part of Sinopharm, China’s state-owned healthcare corporation, which has enjoyed a partnership with GE for more than 30 years.

The new agreement, which is in addition to a ongoing joint venture relationship, will be focused on medical imaging, and will seek to develop, manufacture and commercialise imaging equipment in a bid to meet growing demand in the country. According to a filing by the American firm, the initial focus of the joint venture will be non-premium CT systems and general ultrasound solutions aimed at primary care and rural health markets however, this scope could grow as the venture develops.

Signify Premium Insight: Annalise.ai, Fujifilm and the Perils of Living on the Edge

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Co-written by Dr Sanjay Parekh & Bhvita Jani

Earlier this month Australian AI vendor Annalise.ai announced that it had partnered with Fujifilm Australia to offer its CXR Edge solution on the Japanese vendor’s portable and stationary X-ray machines.

The move will enable Fujifilm to distribute versions of Annalise’s comprehensive decision support solutions that are made to be embedded on medical imaging hardware in Australia, as well as in other select markets such as New Zealand, the United Kingdom and India.  Two variants of Annalise’s CXR Edge software will be available to healthcare providers. One, CXR Edge Comprehensive, can detect 95 clinical findings and is designed for use in inpatient, outpatient, and emergency settings. The second, Annalise CXR Edge Critical Care detects 35 findings, and is designed to be used in trauma, emergency, and intensive care settings.

The Signify View

Time is the enemy when it comes to many medical conditions. As such there is a great premium placed on devices and technologies which can save clinicians time and allow patients to be treated more quickly. While efficiency is an important consideration for all X-ray exams, it is often most important for those conducted using mobile systems, which are frequently used in emergency rooms and in other trauma settings. In these situations, doctors need to be alerted to time-critical conditions (e.g., pneumothorax) quickly, so embedding an AI solution to identify these conditions directly on the modality makes sense. In doing so, possible bottlenecks stemming from processing delays or network reliability issues are sidestepped, allowing doctors to see the results of the AI seconds after acquisition. What’s more, embedding AI tools on the modality itself also avoids the requirement for any additional IT infrastructure (e.g., dedicated servers), a factor that could be particularly beneficial in some emerging markets such as India in which Fujifilm has a strong presence.

Fujifilm is not the first medical imaging vendor to take this approach, GE Healthcare, for example originally released its Critical Care Suite in 2019 which also embedded medical imaging AI on the device. The key difference however are the tools’ remits. GE’s Clinical Care Suite is focused only a smaller number of findings (endotracheal tube placement, pneumothorax triage, quality assurance tools), while the Fujifilm and Annalise combination addresses up to 95 findings. This could prove far more attractive to providers. Mobile x-ray systems are often not dedicated to one specific department, instead being used for multi-disciplinary purposes amongst different clinical applications. Offering a wider range of solutions could therefore be a competitive advantage. This is especially true given the challenge of embedding multiple solutions from multiple vendors on the same device. It is likely unfeasible to have numerous separate solutions embedded on the modality, so a multipurpose, comprehensive system is arguably a more realistic way for providers to benefit from such a wide range of capability.

Patient Waiting

However, despite these advantages, AI solutions embedded on the scanner still have some drawbacks comparted to their PACS-deployed siblings. One of the main disadvantages is that they are unlikely to receive upgrades as frequently. Whereas a PACS-based system will usually receive both feature updates and refinements remotely, frequently bringing both new capabilities and improvements to AI solutions, edge AI solutions are more likely to be left running older versions. This is particularly true if the modality doesn’t have cloud connectivity. This could not only leave radiologists relying on outdated software, but also makes performance issues more likely. This is a particular issue given the nascency of AI adoption and the impressive rate of development.

While Fujifilm is initially offering Annalise’s CXR solution in Australia, it also has plans to offer it in other parts of the world, including India. Here, and in other emerging markets, this lack of updates is unlikely to be a significant issue. On the contrary, these are likely to be some of the best opportunities for embedded AI systems. Many emerging markets have, after all, a distinct shortage of radiologists, and increasing volumes of images. In some situations, having even an outdated version of a comprehensive solution embedded onto an X-ray system could flag critical cases and expedite treatment, rather than the images languishing until a radiologist is available to read them, possibly resulting in missed diagnoses. This is particularly true for the critical care version of the software, which, although detecting 35 findings compared to 95 for the comprehensive solution, is focused on detecting time-sensitive conditions that require immediate treatment.

Earlier is Better

Further, the use of embedded AI is also likely to bring financial benefits to providers. Early detection isn’t only good for patients, it will also facilitate the earlier treatment of patients, which in many cases will save a patient having to undergo more serious, and more expensive treatments.

This will be especially true in single-payor health systems where there is a greater incentive to reduce costs, but private markets such as the US could also benefit. Missed radiological findings can be very costly. Not only because of the added care costs, but also because of the costs of litigation. The fact that the embedded AI tools may help to note findings missed or seen at a more progressive stage by a radiologist, therefore have the potential to save providers considerable amounts of money lost in lawsuits.

Despite this potential, how willing providers will be to pay for such capability remains to be seen. This could depend on how Fujifilm chooses to offer embedded AI. The vendor could add the capability at no extra cost as a way to sweeten a deal and encourage a hardware purchase, alternatively, Fujifilm may choose to only offer it on its highest-end systems in a bid to differentiate the systems in its lineup and encourage customers to spring for the more expensive, higher-tiered systems. Additionally, Fujifilm may also consider upselling these edge solutions across its install base, generating a new source of revenue and enticing potential customers to its AI-enabled premium fleet when their contracts are up for renewal.

Taking the Subscription

The Japanese vendor may also explore some more innovative options such as ongoing subscriptions. Such deals could result in more of a partnership between Fujifilm, Annalise.ai and the customer, ensuring that providers have the support they need to maximise the value they are deriving from the systems. This could also benefit the vendors by turning transactional hardware sales into sticky subscription revenues. It would also overcome the issues of upgrades and would ensure that customers continually benefit from the latest version of the software.

Such a move would be particularly valuable at present. Fujifilm, as with other vendors which offer mobile X-ray systems, capitalised on the demand for the modality during the Covid-19 pandemic. As detailed in Signify Research’s General Radiography and Fluoroscopy Equipment report, global revenues derived from mobile digital radiography systems increased to almost $750m in 2020, up from $413m in 2019 as a result of pandemic-induced demand. Fujifilm, like other vendors will look to continue to capitalise on this spike, and will hope to continue to derive revenues from this expanded install base. Offering AI solutions under a subscription could be one tool that helps the vendor achieve it.

However, regardless of which options are utilised, embedding AI on modalities will not be a huge revenue generator in and of itself for either Fujifilm or Annalise. Fujifilm will boast of the capability to snag some extra deals and upsell to some customers in Australia, but the bigger opportunity lies when it is able to sell its products in India and other emerging markets. Fujifilm has some form in these regions, with its products tending to be more affordable than those of the likes of GE Healthcare, Philips and Siemens Healthineers, while it has also shown it is happy to use innovative methods to try to create business in these areas. In this context, offering embedded AI which seamlessly supplements a limited number of radiologists could be very beneficial. This is particularly true given that Fujifilm has adopted a partnership model. This will give the vendor flexibility to offer different tools in different regions (e.g., Fujifilm has already partnered with Lunit in Mexico and across some regions of South America) or abandon partnerships should better alternatives become available elsewhere.

For Annalise on the other hand, its focus remains on its fuller enterprise-based offering. Offering mobile versions of its solution embedded on mobile X-ray systems will help grow its market share and could serve as an introduction to the company for some providers, but, in isolation, such deals will never allow the vendor to live up to its lofty valuation. Instead, it must focus on selling to providers, with any embedded deals merely an additional revenue stream.

Ultimately it is a smart move, but not one that will have a huge market impact. More significantly, it shows how AI can be embedded into solutions, but these best of breed integrations represent only a small part of a market blessed with numerous approaches. Living on the edge remains a choice not a necessity.

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Signify Premium Insight: The Puzzles of Partnerships – Ultromics & Caption Health Combine

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.

In a recent announcement, AI developers Caption Health and Ultromics revealed that they were entering into a strategic partnership. The move, which will bring together the two firms’ expertise in AI-assisted image acquisition and interpretation, will, according to the vendors, grant more providers the opportunity to perform ultrasound examinations and help them automatically calculate key indicators of heart function. This should facilitate the earlier and more accurate identification of heart disease. The vendors say the partnership will also help bridge the gap between acquisition and diagnosis, with the solutions, alongside hardware from Caption Health partner Butterfly Network, strengthening the capabilities of a greater range of clinicians.

 The Signify View

As Butterfly Network, and other handheld ultrasound vendors seek growth, they are casting their eyes away from established imaging markets and radiology departments and targeting new customers. These companies are looking to introduce ultrasound into both new geographies and to new clinical settings. In doing so, these handheld systems will, for many users represent their first-time using ultrasound, or indeed, any medical imaging system at all.

These new users require support and guidance, vendors and providers have a role in providing this market education, but AI tools can also play their part, helping inexperienced users capture and interpret diagnostic images. This, as Signify Research wrote in August, was the rationale behind Butterfly Network’s partnership with Caption Health. However, given that ultrasound is much more of a ‘real-time’ modality, than others whose images are typically reviewed after acquisition, the ability to capture images alone is of limited clinical value. Caption Health’s partnership with Ultromics aims to address this gap, by allowing those with the Butterfy iQ+ to not only capture images, but also to discern several key cardiac measurements from them.

The partnership improves the clinical value of both developers, as well as that of Butterfly Network, as an entire package comprised of a device, acquisition and analysis tool, the product is arguably more attractive to a new user than any individual component individually.

 A Tough Sell?

While the capabilities of these partners are well aligned, there are some commercial challenges that must be considered. One of these centres on Ultromics and Caption Health’s revenues. If the two vendors’ products are to be sold alongside Butterfly Network’s handheld systems then there is little room for all three vendors to raise reasonable revenues from a device that, according to the IQ+’s webpage, costs customers $102 per month on a 36 month payment plan, including a three year subscription to Butterfly Network’s ‘Pro’ service. The ability to raise these prices is also limited, as doing so will undermine the scanner’s value proposition, one of its key selling points. Providers’ ability to claim the CMS’ new technology add-on payment (NTAP) for the use of Caption Health’s software will sweeten this deal somewhat, but would still leave them having to pay for the devices initially.

Selling the solutions individually could allow the developers to charge higher prices for the tools, but sales would be lower, and these additional costs for the AI tool’s capture and analysis capability would reduce their appeal in the new markets that handheld ultrasound looks to target. Instead, these vendors must rely on high volumes to be successful. This is possible. Butterfly Network’s most recent financial results, for Q3 2021 show product revenue for the quarter of $10.8m. Although there is some ambiguity brought in by products such as cases and straps, the majority of this revenue will come from the vendor’s iQ+ devices, suggesting sales of around 4,500 units for the quarter. With Signify Research forecasting a CAGR of more than 25% for the sector in its latest Ultrasound Equipment report between 2020 and 2025, Butterfly Network could yet be on track to deliver high sales volume.

A reliance on high sales volume may yet prove prudent given the forecast increase in handheld system unit shipments over the coming years

A Preference for Portfolio

This is one area where larger vendors, which offer a full range of ultrasound products could hold an advantage over the handheld specialist vendors. GE and Philips, for example, have both formed partnerships with DiA Imaging Analysis, currently focused on their point of care offerings. However, these vendors could, in future, also open up the partnerships to their broader ultrasound ranges, potentially offering opportunities across a wider range of devices, rather than relying on handheld ultrasound growth. With handheld devices forecasted to account for just 6% of the total ultrasound market by 2025, in the long term this could prove to be a useful option.

A further challenge will be adding further capability to the software tools. The use of Caption Health’s tools to enable new users to capture cardiac ultrasound images will be of limited benefit if those same users don’t have a fully fledged suite of analysis tools. The partnership with Ultromics, and its EchoGo solution brings analysis of ejection fraction, left ventricular volumes and cardiac strain, but the vendor will need to continue to develop its toolkit if it is to make echocardiography truly accessible.

 A Marathon not a Sprint

These challenges, however, don’t take away from the significance of the partnership between Caption Health and Ultromics. The former’s ties to Butterfly Network means that if all works as it should, an inexperienced user can purchase an affordable ultrasound system, perform an examination of a patient’s heart and almost instantaneously garner certain key metrics. Hardware and software has aligned to offer new diagnostic capabilities to whole swathes of new users. Whether this can be done profitably is another matter.

Butterfly Network’s original approach was to develop the majority of its software, as it does with its hardware, in house. The vendor’s partnership with Caption Health signalled an end to this approach, and the use of Ultromics EchoGo system emphasises it. While these moves significantly add to the vendor’s capability and keep it in step with some competitor vendors such as EchoNous, which has recently partnered with US2.ai for cardiac analysis, it does mean sharing revenues with third parties, which, for a vendor focused on affordability, could prove difficult if sales don’t live up to expectations. Moreover, Butterfly Network is potentially losing out on the opportunity to upsell AI software applications and services compared with competitors such as Clarius who have a stronger focus on developing their own applications.

Despite this, the move remains sound for the AI developers. Sales of relatively narrow AI solutions directly to providers could prove challenging, and direct sales to users in new markets doubly so. By partnering to create a package that is more clinically valuable, the vendors are able to strengthen Butterfly Network’s commercial proposition whilst leveraging it as a potentially far-reaching sales channel.

For handheld vendors such as Butterfly Network and imaging AI developers alike, this increasingly collaborative approach is not a magic bullet. This seems well understood by Ultromics in particular, which, unlike Caption Health, did not enter into an exclusive partnership, suggesting that the vendor sees the handheld market as an additional, rather than integral revenue stream.

As such, collaboration will not completely solve all the issues that the relatively young segment is facing. Despite the AI assistance, market education is still a hurdle to adoption, technical barriers remain with image quality potentially hindering the usefulness of the tools, and other factors, such as the inability of a provider to act upon the results of an exam could undermine the utility of AI-equipped handheld systems in new settings.

But, as the value of the solutions is bolstered by developments such as Ultromics’ and Caption Health’s partnership, the motivation to address the other challenges will increase. The first pieces are being laid, now the rest of the puzzle can start to fall into place.

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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: Aidoc’s Platform Partners Show the Way Ahead

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.

Co-written by Dr Sanjay Parekh

Aidoc has continued to forge partnerships to expand its coverage of radiological specialities, and last week added ScreenPoint Medical to its roster. The collaboration will see the mammography AI vendor’s toolset added into Aidoc’s platform, allowing breast imaging specialists to access its capability within the existing workflow.

Aidoc has been increasingly utilising partnerships to complement its own AI tools and expand the capability it offers its customers. The addition of ScreenPoint to Aidoc’s stable will mean that the Israeli vendor’s customers will now be able to utilise ScreenPoint’s Transpara breast decision support solution, alongside solutions from its other partners, which include Imbio, icometrix, and Riverain Technologies for pulmonary embolism, stroke care, and lung cancer respectively.

The Signify View

AI will, over the coming years, transform medical imaging. Tools to supplement radiologists in their diagnoses, streamline radiology workflows and positively impact clinical outcomes will become ubiquitous. However, in order to reap any of these benefits, these tools must first be incorporated into existing radiology workflows.

One of the ways this challenge is being addressed is through the use of AI platforms. Such platforms facilitate the deployment and integration of AI tools into the PACS, making it easier for radiologists to use and manage multiple AI tools within the imaging workflow. Vendors of third-party AI marketplaces, which facilitate the selection and purchasing of algorithms, such as Terarecon and Blackford Analysis were among the first companies to offer these platforms as a way for providers to efficiently deploy the algorithms purchased through their marketplaces.

As AI developers expanded their portfolios and began offering a greater range of algorithms, challenges around integration remained. Some vendors, such as Aidoc, have also chosen to go down this route (and develop native platform capabilities), enabling multiple AI solutions to be deployed through a single platform rather than having to be brought into radiologists’ workflows individually.  Initially these platforms from algorithm developers started as a way to deploy their own algorithms into providers’ workflows, however, the market has evolved, and providers are seeking more complete solutions than Aidoc alone can offer. This has prompted Aidoc to form partnerships to enable it to quickly expand beyond its original brief of detection and triage for certain CT exams. It now offers several complete end-to-end solutions for a number of high-value clinical use cases, including pulmonary embolism care, and stroke care.

Functional Focus

The flurry of recent partnerships made by Aidoc, as well as its continued development of native algorithms has been integral in its ability to offer these end-to-end solutions. Its partnership with Imbio in December 2020 provided quantification capabilities as part of its pulmonary embolism care solution, and its partnership with icometrix in June added perfusion capabilities to its stroke care coordination solution. In September, Aidoc partnered with Subtle Medical, to bring image enhancement capabilities for image acquisition. Last month, the vendor partnered with Riverain Technologies to take advantage of its chest AI solutions (which improve efficiency and improve nodule detection rates), while most recently, the collaboration with ScreenPoint Medical aids its breast imaging play.

These partnerships have enabled Aidoc to bundle tools that address specific clinical use cases. One challenge in this approach, however, will be addressing gaps beyond its existing capabilities. Unlike some vendors that promise to offer ‘comprehensive’ solutions, which ‘solve’ a particular body area/modality combination, Aidoc’s focus on specific use cases mean that for anything outside of that specific scenario, radiologists will have to still read the image themselves, or seek out other algorithms to supplement the offerings available on Aidoc’s platform. While this situation will improve over time as Aidoc adds more partners and develops more native algorithms, it is, at present, a limitation.

The impact of this limitation will vary by provider. One key metric for radiology reading groups, for example, is their efficiency and turnaround times. This is particularly true for emergency cases and night reads. As such, despite the gaps in its capability, if Aidoc can help improve turnaround times for these reading groups and thereby give them a competitive advantage, its tools will still be sought after. This competitive advantage will also enable radiology groups to compete with their peers (other radiology groups) more aggressively as well as with hospitals themselves, thereby generating further business.

This perhaps, is one of the reasons Aidoc felt another of its partnerships made sense, that with one of the leading radiology groups in the US, Radiology Partners. At the time of the partnership (April 2021), it was touted as the largest clinical deployment of AI in healthcare, affecting as many as 40 million annual exams.

Standing Out

Aidoc’s focus on use cases is one factor which differentiates the vendor from its closest competitors, such as Arterys, which instead bundles algorithms by body area and modality. Arterys arrived at this approach after setting out to compete with other marketplaces. The vendor’s initial focus was establishing a wealth of third-party AI developers to host in its marketplace and on its platform. The bundled offerings came later, as it sought to add greater value for its customers. This is in contrast to Aidoc, which has prioritised providing solutions which address an entire clinical workflow for a given use case; a method which potentially harbours more commercial potential given that a provider may now be more willing to pay for a fully-fledged solution, at a higher price, compared to a single tool.

Another difference is that as well as offering a platform which hosts a number of both native and third-party AI solutions, Arterys’ platform also includes a web-based viewer, which the vendor says negates the need for a PACS viewer from an imaging informatics vendor. Users of Aidoc’s platform, meanwhile, will still require a traditional PACS from another vendor. This has potential to be a challenge for Aidoc, given that many of the large imaging IT vendors also offer AI platforms which could offer similar functionality to Aidoc’s. As such, why would a provider use a third-party AI platform when it already has one as part of its imaging IT system?

To convince these providers to use its own platform, Aidoc will need to demonstrate the additional value it brings to a provider. The fact that, through its own in-house development and its partnership with third parties, it has been able to curate solutions that address entire use cases adds value beyond what an imaging IT vendor’s platform might readily offer. Aidoc’s direct-to-customer sales strategy and platform play enables its customers to more easily scale up their AI offerings, rather than the provider having to do the extra legwork of integrating a number of different AI tools from different vendors to achieve the same result.

Furthermore, should providers be more willing to use Aidoc’s platform given the value it provides, regardless of their PACS vendor, larger imaging IT companies could also be motivated to integrate the AI developer’s offering into their systems. This would, after all, immediately add considerable functionality to an IT vendor’s system, rather than it having to forge numerous separate partnerships with a whole host of individual AI developers.

A further argument Aidoc will have in its favour is its range of native algorithms. If Aidoc can establish the reputation of its own solutions and position the tools it gets from its partners as a supplement to these native tools, highlighting that they have all been validated for its own platform, then the fact it has already completed some of the heavy lifting could also fall in its favour.

Aidoc’s Calling

Aidoc’s success with this strategy remains to be seen, but its continuing drive for broader solutions comprised of both native and third-party algorithms and deployed via its own platform does highlight the evolution in the medical imaging AI market.

It has become apparent that it is not viable in the long term to be a company whose only products are narrow, single purpose algorithms. While this may have been feasible several years ago due to the novelty and hype surrounding deep learning, vendors now need to offer more complete solutions that solve providers’ problems. Partnerships are for many vendors such as Aidoc, a preferred way to achieve this, as it enables the vendor to leverage another vendor’s expertise and investment for the benefit of its own customers, and scale its solutions much more rapidly. The progression of some AI developers, from being a provider of AI tools to one with a portfolio of AI solutions and maybe a platform, is, in this light, a response to the challenge of getting these complete solutions to customers, and as such it is increasingly necessary to consider.

Aidoc and its ilk’s biggest challenge may be retaining relevance in the face of an increasingly saturated market for AI platforms. However, by giving providers carefully curated AI solutions that address specific use cases, while also enabling them to take advantage of the innovative, best of breed solutions from other AI vendors, Aidoc may yet be able to carve out its own segment. It will need to both continue to develop its own capabilities (native AI solutions and platform) and continue to carefully curate a band of partners which will enable it to comprehensively address certain cases, but in doing so Aidoc may have found its calling. This vendor’s course has been set.

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