Tag Archives: Comprehensive

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

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 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.

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