Tag Archives: Platforms

Signify Premium Insight: Welcome to the Jungle – Trends in the AI Ecosystem

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Last month saw the release of Signify Research’s Competitor Ecosystem topical report, one of the deliverables of the AI in Medical Imaging Market Intelligence Service. With the medical imaging AI market evolving, there are several trends that are standing out and impacting the complexion of the market. Such maturation has left vendors with striking opportunities in the market, as well as some significant challenges.

The Signify View

One of the most dramatic indicators of the development of the market is the ability of some vendors to raise investment to levels almost unimaginable just several years ago. Tellingly, the nature of investment is also changing. Previously, the focus of private investors centered around smaller, earlier-stage funding rounds for young start-ups. More recently, however, the emphasis for investors has shifted toward supporting larger, better-established vendors with more sizable later-stage rounds.

This trend has given rise to a select group of vendors. Companies which, by one metric at least are market leaders, having raised more than $100m in investor funding.

While the number of vendors in this rarefied air is increasing, even among these investor favourites, exceptional performers are starting to emerge, with several vendors having individual funding rounds of more than $100m. Rounds that, individually, dwarf the total amount of funding that more than 95 percent of any other companies have been able to raise in total. This level of investment, propelling this select group of vendors forward highlights the confidence investors have in these companies as businesses, not just technological innovators. Such sizable later stage investment shows that investors realise that select AI firms not only have compelling technology, but also a robust product portfolio and represent a strong value proposition. There are exceptions to this trend, with some, primarily Chinese, vendors benefitting from government incentives and support, but in most cases, such impressive levels of investment demonstrate vendors that have been able to turn technology into business.

It’s Getting Later

In addition to consolidating the position of market leaders, this trend of private investment increasingly focusing on later stage funding rounds and already established vendors means that smaller vendors will find it ever more difficult to secure funding. Over time this will leave them facing difficult and potentially desperate decisions. In a developing tale of the haves and the have-nots, smaller vendors which have yet to gain commercial traction or develop sophisticated solutions with their core technologies will face shrinking funding runways. As this happens, they will cease to be able to afford the costly product development and expensive clinical validation studies that will enable them to grow and rally. Over time, these shrinking runways will lead to dramatic market consolidation as vendors become more open to acquisitions, pivots, or, if necessary, dissolution.

This consolidation around the existent leaders is happening in other ways too. While demonstrating their commercial potential to investors, financially well-supported vendors have also shown their market leadership calibre in another way. Many of these AI developers are increasingly able to tout not just regulatory clearances, a milestone which essentially demonstrates that products work safely and as intended, but also reimbursement. This is significant. One of the most difficult barriers for vendors to overcome is that of convincing providers to pay for their solutions. While reimbursement does not necessarily make providers money, it offsets or at least mitigates, to some extent, the cost providers must pay to take advantage of AI solutions.

The awarding of reimbursement to solutions, combined with the capabilities of the tools themselves, will help motivate providers to adopt and help AI become an increasingly mainstream tool. This will help consolidate certain providers’ positions as market leaders, and set those vendors that have failed to innovate, even those that started strongly, further back in their paths to reach market leadership positions. This could be particularly true as market leading vendors look to expand the breadth of their portfolios, potentially encroaching on markets targeted by smaller competitors.

The Guiding Hand

Reimbursement also has the potential to be transformative in other ways. In addition to mitigating the cost barrier stymieing adoption of medical imaging AI at providers, through reimbursement, regulators can also guide the markets they oversee, encouraging and essentially subsidising development in certain directions. This has recently been apparent in the US, where reimbursement has been awarded to solutions such as Cleerly’s cardiac plaque detection algorithm and Optellum’s tissue characterisation algorithm. While both tools are very different and have very different clinical uses, the fact that both offer advantages in a broader, population health context, rather than simply offering advantages in very specific contexts with limited downstream impact will no doubt have helped solidify their case for reimbursement.

Further by offering reimbursement for AI solutions that also offer advantages in a broader population health context, vendors will be encouraged to address this consideration as they continue to develop their solutions. There are similar motivations with regards to other tools, with, for example, several solutions which have received reimbursement reshaping the established diagnostic pathway, allowing a shorter time to treatment, in the case of stroke algorithms, or, in some cases, eliminating the requirement for invasive diagnostic procedures, as is the case for FFR-CT.

Platform Progress

Reimbursement is helping to overcome the cost barrier that is holding back the adoption of medical imaging AI, but there are also other challenges slowing the pace of the technology’s uptake. One of these can be the difficulty of deploying AI into the clinical workflow. AI platforms have become increasingly common as vendors look to solve the last-mile challenges of deployment, integration, and orchestration.

As these platforms continue on their way to ubiquity, they are, like the solutions they deliver, also becoming increasingly sophisticated. Many platforms initially served the relatively straightforward purpose of becoming a means to host applications and making them easily accessible to providers. Latterly, however, platforms are serving more complex services. One trend, for example, has seen algorithm developers themselves begin to offer commercial platforms.

This is a logical progression, with algorithm developers having to essentially offer platforms as their range of native tools grew and they needed an efficient way to be able to deploy them all into providers’ workflows. Some vendors have expanded this functionality commercially, hosting third-party algorithms alongside their own natively developed solutions. By bolstering their platforms in such a way these algorithm developers can further improve the clinical utility of their offerings, using third-party applications to supplement their own natively-developed capability, and in doing so offer curated packages and workflow suites tailored to particular clinical workflows.

Instead of simply deploying AI into workflows, these more sophisticated, better curated platforms can orchestrate algorithms, to not only deliver capability, but ensure it can be effectively utilised. Over time, in many cases these platforms, whether natively developed by informatics vendors, specialist platform providers, algorithm developers or even modality vendors, will replace the direct integrations that characterised the early days of medical imaging AI adoption. In this way platform providers will harbour increasing sway over the medical imaging AI market.

Acquisitive Rationale

This trend could begin giving large imaging IT vendors reason to start making acquisitions, beyond simply acquiring specific AI capabilities. This, combined with the increasing competition for providers’ dollars and funding challenges, will hasten consolidation in the market.

There have been some early signs that this consolidation is starting to bite, including MaxQ.ai’s pivot away from the medical imaging AI market, Nanox’s acquisition of Zebra Medical Vision and more recently Tempus’ acquisition of Arterys. Such headlines are likely to become more common in 2023 and beyond as it becomes increasingly difficult to compete with the established cohort of market leaders and their more sophisticated solutions. This impetus is also likely to give rise to other trends; vendors turning away from radiology to other markets where their capabilities might be in higher demand, or they are not hindered by the same regulatory hurdles. Vendors may, for example, look towards pharmaceuticals and drug discovery.

Ultimately, these shifts in medical imaging AI could leave the market drastically changed in several years. Fewer vendors, with broader capabilities, smaller vendors acquired and subsumed by larger market leaders, healthy reimbursement, and true mainstream adoption, even several unicorns traded publicly. Regardless of these potential changes, the foundation of the successful companies will remain the same. The companies that have success in the future will still be the ones that can offer, evidence, and deliver clinical value. Essentially, vendors capable of delivering on AI’s fundamental promise will continue to thrive.

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Signify Premium Insight: Annalise Hoping to get Comprehensively 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

Annalise AI recently announced that it has launched an AI-powered decision support solution for non-contrast CT brain studies. The Australian company boasts that its new solution, dubbed Annalise CTB, is the most clinically comprehensive brain CT solution available, and can identify 130 findings.

The tool continues along the strategic path established by Annalise with its comprehensive chest X-ray solution, with a product strategy focused on detection of all radiologically relevant findings for a given body area or scan type, therefore more closely mimicking a radiologist’s process. As such, Annalise hopes its comprehensive solutions will be more clinically useful than those offered by many of its peers, who have tended to provide ‘point’ solutions for a single or small group of radiological findings.

The Signify View

When Annalise AI launched Annalise CXR, its chest X-ray solution, it immediately attracted the attention of all who study the medical imaging AI market. It did so because of the number 124; the number of findings the solution could detect. While there is some nuance, this figure was markedly higher than that of the most comparable vendors operating in the market. At 124, rather than the 10, 20 or even 50 findings claimed by competitors, Annalise made clear it was adopting a different approach to medical imaging AI.

Many vendors, particularly in the earlier days of medical imaging AI, were preoccupied with improving the sensitivity and specificity of the detection of a single radiological finding. While there are some scenarios in which such competent ‘point’ solutions are valuable, for the most part they offered only incremental gains compared to an unassisted radiologist, while frequently disrupting that radiologist’s workflow. Furthermore, the radiologist would often still need to thoroughly read a scan, to look for every other finding that the algorithm was not searching for. The benefits, in short, often failed to outweigh the drawbacks.

Various approaches to this challenge were adopted by vendors, many sought to expand their product’s utility along a clinical workflow, turning their algorithms into just one component of an expanded solution. Annalise, on the other hand, sought to expand its breadth of capabilities across multiple findings. Significantly, aiming to detect all possible findings for a single modality/body area combination. In doing so, the tool would more closely resemble the approach of radiologists. This would offer greater clinical value, also helping identify incidental findings and expediting the read for a radiologist.


As with Annalise’s CXR solution, this philosophy permeates the vendor’s head CT solution, which identifies numerous findings, including those related to the brain, such as brain bleeds and midline shifts, as well as to the head more broadly, with, for example, eye orbits and paranasal sinuses both assessed, as well as findings on the scalp and neck. This breadth expands upon the focus of many of Annalise’s competitors, which often only address findings for the brain. Annalise’s solution, which addresses a broader range, could therefore prove attractive to providers, particularly when some time-consuming reads, such as C-spine assessment, are considered.

Beyond tackling some of these exams that are less well catered for, the adoption of comprehensive solutions can also herald an approach more focused on population health. In mimicking a radiologist, comprehensive solutions can help avoid missing findings, particularly those that are not part of the primary diagnosis, enabling patients to be put on a treatment pathway for these incidentals as well as primary findings. In doing so, missed diagnoses or misdiagnoses can be reduced enabling patients to be treated sooner and outcomes to be improved.

While such population health advantages can be valuable, their impact will be most significant in single payer markets, where the payer and provider, represent the same entity. In such a system, regardless of where or when the patient continues treatment, any downstream savings made and any reduction in care costs over the longer term will ultimately benefit the same payer. Such an advantage cannot be conferred in predominantly private markets, where there is no guarantee that identifying additional findings in a scan will bring benefits to the same provider. Instead, allocating resource on an AI solution may only benefit a different  provider, where the patient eventually seeks treatment.

Another similar challenge in private markets will be in convincing providers to utilise comprehensive solutions. Although they may have some clinical advantages, for providers it is often advantageous to conduct, and therefore bill for, multiple specialist scans, rather than a single, comprehensive scan. As such, there may be limited motivation among providers to adopt such tools.

Regulatory Burden

Adoption of Annalise’s Head CT solution, along with other comprehensive tools, also faces another challenge in the world’s largest private healthcare market, the US: regulation. While Annalise’s solutions have received regulatory clearance in Europe and Australia as a single comprehensive tool, in the US, the FDA has held-out, insisting that each of a solution’s findings are treated as if a narrow, point solution. Each finding must, in effect, be treated as a single product.

The rationale behind such an approach is that each individual finding promised by a comprehensive solution should be subjected to the same regulatory rigour as a point solution, thereby ensuring that a comprehensive solution can demonstrably perform as effectively as an approved point solution in any single task. In Europe, conversely, comprehensive solutions have been regulatorily palatable provided they meet a minimum viable threshold.

There are merits to each of these approaches. The US FDA’s demanding criteria is, in the short term at least, arguably good for clinical practice. It ensures patient safety, minimising the opportunity for misdiagnosis, and prioritises patient outcomes. But, it is a regulatory framework that will stifle innovation, and in the longer-term prevent US patients benefiting from some tools. While these high barriers are appropriate in some cases, such as common findings where there are considerable training data, they severely hamper vendors’ abilities to address more obscure findings targeted in Annalise’s CTB, in the paranasal sinuses or in the pineal gland, for example.

There are some routes that purveyors of comprehensive algorithms for more obscure findings can take in the US. They could, for example, seek approval via the triage (CADt) rather than detection (CADe) regulatory pathway; a more straightforward route to market. Another option for vendors offering comprehensive solutions is to break up their offerings in the US, only offering the specific algorithms which have been approved. Both these approaches may help a vendor get a toe in the market, but neither are ideal, both potentially robbing solutions of their strengths.

Diverging Details

Despite these drawbacks, these are the concessions that Annalise is likely to have to make if it seeks to gain ground in the US. There is no reason to expect that either of the vendor’s solutions are to be any less well received than its chest X-ray solution has been in Europe and Australia. However, if it is to establish a footprint in the US, the vendor will have to take on the more time-consuming piecemeal approach to approval, beginning with the most clinically common solutions, before working its way through its broader array of findings.

The ramifications of such a requirement could, over time be significant. In Europe and Australia, comprehensive solutions could flourish, becoming providers’ preferred methods of AI adoption. In the US however, the FDA’s approach could mean that platforms which offer one or multiple suites for certain clinical use cases could become the norm. Enabling a range of vendors, each focused on their own regulatory challenges, to effectively be offered together through a platform to provide hospitals with a useful breadth of capability.

Although in some sense these platforms appear antithetical to Annalise’s comprehensive approach, they could themselves be an opportunity. Like Aidoc before it, Annalise may choose to offer a platform itself, including a version of CXR and CTB, which has been cut down to secure regulatory approval, alongside some other solutions from partner vendors. Over time though, as Annalise receives approval for more findings, and releases other products with different focuses based on modality, body area or clinical focus, it could incorporate them into its own platform displacing third parties. As such, it could adopt a gradual approach to the US market while capitalising on the different regulatory frameworks elsewhere.

As ever, there are varying routes to success, and in this nascent market, there is no certainty about which one is preferable. Other leading vendors have made their mark in their own unique ways, with, for example, Heartflow and Cleerly changing the diagnostic pathway for diagnosing patients with coronary artery disease, Viz.ai, RapidAI, and others altering care pathways for stroke care, and Perspectum helping reduce the need for liver biopsies. Similarly, Annalise, with its head CT solution, emphasises its intent to be the top comprehensive solution.

Other vendors are following a similar path, some with considerable advantages in other settings – one only need look at Lunit’s breadth of partners to see the esteem in which that firm is held – but Annalise has ensured that providers must at least consider its comprehensive potential.

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Signify Premium Insight: What to Expect at RSNA 2022 – Imaging IT and 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.

Much change is afoot in the medical imaging IT and AI sectors. Imaging IT vendors are transitioning to broader, integrated cloud-native enterprise imaging solutions, while AI vendors are increasing the sophistication and clinical value and utility of their products. Amidst these broader directions, here are our  key expectations for the show.

Imaging IT and AI Vendors Will Have Lots to Share

While this year’s meeting of the Radiological Society of North America might be somewhat subdued from a modality standpoint, imaging IT and AI vendors will be ready for some significant product launches. Unlike last year when Covid-19 was still rife in many countries, and there were still considerable restrictions on travel across the world, this year’s event should see attendance figures much closer to the pre-pandemic levels of 2019. Such enthusiasm should see vendors willing to make a lager  investment into the show and use it as an opportunity to promote high-profile new products.

Imaging IT vendors will be keen to highlight the progress made in developing fully featured, cloud-native enterprise imaging platforms. In some cases, this will be demonstrating additional capability that has been added to an existent platform, while other vendors may, for the first time, demonstrate how different capability across their portfolios can be integrated into a more complete enterprise imaging solution.

The event is also likely to be busy from an AI perspective, with vendors keen to promote their products’ progression from technology to solution. AI outfits will look to demonstrate new, more sophisticated solutions, which address a greater number of clinical segments and integrate more seamlessly into providers’ workflows.

Efficiency and Optimisation

Between unprecedented backlogs of patients whose procedures were delayed during the Covid-19 pandemic, a shortage of trained personnel in many key roles, and the increasing requirement for more time consuming and resource intensive advanced imaging procedures, providers are looking for ways to do more with less.

Imaging IT vendors at RSNA will be highlighting solutions that offer customers greater oversight of the operation of their medical imaging departments, their staff, and their medical imaging modality fleets. This focus will be reciprocated by providers, many of whom will adopt workflow tools as one of the nearest-term investments to enhance productivity. Such solutions also represent a sensible strategy for providers for the longer term, enabling them to better assess departmental performance, improve planning of patient care, strategize future needs and maximise resource allocation.

This is particularly important as provider networks become more complex, with centralised workflows allowing providers better oversight of increasingly decentralised networks, amidst the increasing utilisation of outpatient facilities and teleradiology service providers. This may also facilitate the expansion of provider networks through increasing acquisitions, and enable more sophisticated tools, which leverage AI, to be deployed. The interoperability offered by these holistic systems will help empower provider networks for increased automation and operational AI.

Platforms, Platforms, Platforms

Despite the greater interest and greater practical utility of AI, the young technology still faces some barriers to greater adoption. One of these barriers is a means to deploy AI into providers’ clinical workflows as providers look to scale their radiology AI offerings. The most visible method of addressing this “last-mile challenge” at RSNA will be through platforms.

Several vendors have already released platforms, including third-party incumbents such as Blackford Analysis and Terarecon, but more recently specialist AI companies, larger medical imaging IT vendors, and even hardware vendors have released platforms that support the use of many different algorithms. While plans from major international imaging vendors and imaging IT vendors have so far had the most momentum, platforms from algorithm developers themselves could also be a prominent feature of RSNA this year.

This excitement surrounding AI platforms is also likely to shape many of the conversations that AI vendors have with one another. In past years, vendors may have been looking to forge standalone partnerships with other AI vendors which offer complementary solutions in a bid to offer providers solutions more clinically valuable than either partner could supply alone. While that may still make sense in some use cases, in other cases some of the more established AI independent software vendors will look to forge partnerships with multiple prospective partnersto facilitate the development of a platform and scale their radiology AI offerings. This is especially true given that vendors are increasingly focused on enhancing their product capabilities natively, rather than leverage third parties, as may have been more prevalent in previous years.

The Consolidation of Data

One of the longer-term strategic directions that is set to shape imaging informatics over the coming years is the consolidation of data.

As imaging IT vendors’ multi-ology enterprise imaging strategies evolve, there is a greater need for enterprise-wide data to be consolidated into a central data management platform or the VNA. Doing so will enable providers to better leverage the breadth of data they have. While data management platforms are not a conceptually new product, vendors are beginning to assess how providers can leverage the centralised platform and explore the potential they offer. As such, there are likely to be few flashy announcements associated with the VNA. Instead, vendors will, behind the scenes, be discussing it with their customers to identify opportunities that could be realised.

This will be particularly true given the wider context affecting providers at present. The lasting impact of the Covid-19 pandemic, along with other economic pressures, such as rising inflation and spiralling energy costs mean that hospital budgets will, in many instances, be getting tighter. In such circumstances providers are going to be increasingly keen to monetize the data they have already. For providers, this could mean utilising their wealth of patient data for clinical trials or drug development, for example, or utilising their imaging data to develop AI in house. For this to be a realistic possibility, vendors need to respond and offer sophisticated platforms that properly structure and curate data in formats that allow for the commercialisation of imaging and non-imaging data, including deidentification of patient information for pre-clinical use.

There has already been progress on this front. GE HealthCare’s partnership with Enlitic, for example, emphasises this curation, while Intelerad’s recent acquisition of Life Image also shows that it is an emerging trend in the imaging IT market. While there may not be any blockbuster announcements, vendors will be keen to highlight the importance and potential of these unified data management platforms to prospects at the show.

AI Beyond Radiology

So far, the primary focus of the majority of medical imaging AI has been radiology. However, as AI is maturing, and many radiology AI solutions are becoming more sophisticated, medical imaging AI’s domain will expand beyond radiology. This will see the technology’s purview increasingly grow into adjacent areas such as population health tools that may be deployed as part of screening programmes or identifying incidental findings as part of routine clinical practice.

Such moves, forming key discussion topics for AI vendors at RSNA, represents AI’s growing maturity, and the evolution of AI algorithms into more sophisticated solutions. Such momentum stems from two distinct sources. Firstly, this evolution represents vendors’ need to continue to develop their products to create ever greater value to radiologists, and in return, drive commercial traction. More interestingly, however, is vendors’ plans to tap into the current wave of Category III CPT codes awarded for quantitative imaging AI solutions, which could be indicative of potential future reimbursement.

Many of these CPT codes announced for 2022 do not focus on the traditional domains of radiology AI such as detection and triage, but instead seemingly promote population health applications. This emphasis will entice vendors to position their solutions to leverage these codes in the hope that over the longer term they are upgraded to qualify for tangible reimbursement. But, such leanings also raise the expectation that more population health-focused codes are expected in the coming years, thereby encouraging vendors to increasingly develop population health solutions, or adapt their current solutions to fulfill a population health remit.


Medical imaging IT and AI markets are evolving quickly, and the RNSA conference allows vendors to, above all else, highlight their progress in several key areas. Of equal importance, however, is what isn’t on display, but what is said. Many providers will be looking to commit to enterprise imaging solutions, cloud strategies and AI adoption over the coming years, and vendors’ presence and messaging could help to influence their approach. Ultimately, vendors have the chance to explain to these providers how the application of their solutions can solve pertinent problems in radiology and beyond.

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