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Signify Premium Insight: Cleerly Exceptional: $223m Funding lets Heart Health Specialist Join AI’s Top Table

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Co-written by Dr Sanjay Parekh

Cleerly, the cardiac AI vendor which emerged from stealth last year with a $43m Series B funding round, has seen its total funding increase five-fold thanks to a bumper Series C round. The vendor, which had previously raised a total of $56m, closed its Series C financing round of $223m in late July, taking its total funding to $279m.

The round, which places Cleerly among the very upper echelon of AI vendors in terms of funding, will allow the vendor to expand its team, while also allowing the AI outfit to extend its commercial reach, granting more providers and patients access to the tool. Despite investors’ confidence, however, does Cleerly, still a very young and unproven vendor, really warrant such financial backing?

The Signify View

Cleerly is unusual. Aside from the fact that it has taken one of the largest ever sums in a single funding round for a medical imaging AI vendor, and that it has done so at an unprecedented pace, it does not easily fit into the same categories as other vendors with similar funding totals. For the most part, vendors which can boast of more than $100m in total funding fall into one of two categories. Either they offer multiple or divested products, offering providers a breadth of capability, or a depth of capability along a clinical use case, or they are from China. Cleerly, like HeartFlow which has raised more than $550m in funding, does not fit easily into either, currently offering only a single cardiac tool.

This, however, is a tool which could have a significant impact. Heart disease is one of the world’s greatest health burdens, but all too frequently it is only discovered when a patient suffers a major cardiac event. If the patient survives, treatment usually involves expensive invasive and/or pharmaceutical interventions. Assessing heart health before a patient experiences serious symptoms can also be difficult, and is usually dependent on tests for surrogate biomarkers (e.g., cholesterol), or sequelae of the disease (e.g., ischaemia or stenosis). Cleerly’s solution aims to end this shaky reliance and instead, directly measure, quantify, and classify atherosclerosis itself.

Individual Symptoms

This can, in the first instance, provide value by aiding diagnosis. A patient may present with chest pain, for example, and Cleerly’s solution would be able to ascertain whether that patient has an increased risk of cardiovascular disease, which results in the patient having an elevated risk of a more serious cardiac event in the future, or whether it was a single transient incident that required lifestyle management rather than pharmaceutical or surgical intervention. Being able to accurately determine a symptomatic patient’s cardiovascular health would therefore be highly valuable to a provider.

This value looks liable to increase in the short term too. Optellum, a vendor focused on lung nodule malignancy detection recently demonstrated that its Virtual Nodule Clinic solution qualifies for CPT code 0721T for quantitative CT tissue characterisation, thereby granting reimbursement for the solution’s use. Cleerly’s tool is in some ways similar, quantifying tissue from a CT scan in order to assess a patient’s risk of a serious pathology (measuring, quantifying, and classifying plaque). It may therefore be likely that Cleerly will pursue use of this CPT code, deeming its solution eligible for reimbursement and pushing for this in the near future. This would overcome one of the main obstacles holding back the use of AI solutions, at hospitals making sales look even more promising. As such, working to attain such reimbursement should be among Cleerly’s priorities.

This however misses the true potential of Cleerly’s solution, the potential that prompted investors to pull out their cheque books so readily.

A Bolder Vision

Cleerly states that it envisions “a world without heart attacks”. This will not be achieved by the better management of patients showing up at emergency rooms with chest pain. Instead, this vision ties into the more significant opportunity offered by solutions such as that developed by Cleerly, which helps explain investors’ enthusiasm.

Instead of looking to only help in the diagnosis of symptomatic patients (on an individual case basis), Cleerly’s solution may be used to help manage the health of entire populations. Instead of waiting for patients to present with symptoms related to heart health, Cleery’s solution could be used for opportunistic screening, and used in the background for every patient that has  a chest CT. In doing so, those at a high risk of cardiovascular disease, even in the absence of immediate symptoms, could be given help sooner, and their yet-to-manifest serious cardiac event could be tempered or even averted through lifestyle changes or preventative medicine.

The real prize from such an approach, however, is realised when providers are able to not only treat  those who have been scanned in response to symptoms or surrogate biomarkers, but when the use of a solution such as Cleerly’s on similar patient cohorts can determine at-risk patients that would benefit from earlier intervention.

To achieve this, the detailed quantification profiles provided by Cleerly need to be fed back into the EMR or an enterprise data warehouse (EDW). Once in the EMR/EDW, vendors specialising in risk stratification such as Innovaccer, Health Catalyst and Arcadia would be able to utilise the data in their own algorithms to identify and guide the management of these specific patient cohorts. In this way, patients within the EMR, who may have never had any cardiac symptoms or any related condition, could be identified as a high risk and have their health proactively managed.

Action from a Distance

There are currently barriers to such an approach. Some of these are centred around technical challenges or issues to do with implementation. For instance, there needs to be closer integration of radiology data into the EMR/EDW, with the results of Cleerly’s quantification being not only collected in the radiologist’s report, but also imported into a structured data field within the EMR/EDW. For EMR/EDW vendors to focus on such a feature there needs to be demand from customers, and cardiac plaque scores to be collected widely enough to be worth the developmental cost. The lack of established cardiac screening programmes makes this even more challenging. The same is true for the risk stratification specialists, which will only start considering scores from the likes of Cleerly when they are proven enough to have meaningful impact. Although, of course, having an impact is one of the factors that will drive adoption.

Other factors are financial. In the key US market, without adoption of value-based care payment models, providers could face lost revenue if such tools reduce volumes of lucrative interventional procedures. These value-based care payments are increasing in prevalence, accounting for 41% share in 2020, up from just 23% in 2015, but there is still some way to go. Cleerly may also argue that such adoption of its tool could offset loss of revenue from valuable interventions and prescriptions for providers. It could, for instance, highlight that its tool may identify greater numbers of patients that require lower cost treatments, as well as some that may still benefit from surgical or pharmaceutical intervention. Cleerly might also explain that many lucrative procedures would ultimately be delayed rather than completely averted.

Benefit and Burden

These, however, are questions for the future. In the near term there is clearly enough value for providers to consider the adoption of the company’s solutions, this will be especially true if its solution receives reimbursement. For investors, this clinical value is a good indicator of an AI vendor that is worth funding. This, in combination with other longer-term opportunities such as adoption in a comprehensive population health management programme, and the vendor’s involvement in large-scale international, long-term clinical studies of cardiovascular disease mean the vendor is ensuring it harbours significant revenue-generating potential in the future.

There are challenges. Both from within, if it fails to maintain its pace of development, for example, and externally. These outward challenges include increased competition with comparable solutions from the burgeoning range of AI specialists focused on heart health such as HeartFlow, Keya Medical, Artrya, and Caristo Diagnostics, as well as the barriers that, at present, prevent Cleerly’s tool maximising its utility in population health management deployments. Another, more nuanced concern is related to the valuation of the company conferred on it by such an enormous funding round. HeartFlow failed in its attempt to list publicly, in part because of disagreements over the company’s true worth. Securing such sizable funding could be necessary for Cleerly to grow, while other factors such as the extensive international clinical study Cleerly is funding could prove to be a veritable goldmine in years to come, but in the near to mid-term, when revenues and especially profits will be slender, such significant funding at such an early stage could quickly become a burden.

Cleerly’s ascent has been remarkable. The speed at which it has travelled from an unknown developer in stealth-mode to join the ranks of the best-funded AI companies in the industry has been unprecedented. That makes sense if the vendor one day can, as it hopes, make heart attacks a thing of the past. Until then, it highlights what more the vendor has to prove.

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Signify Premium Insight: Canadian Firm Pockets Cash to Give Patients Their Images

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Last month PocketHealth raised US$16m in a Series A funding round that will be used to increase the medical imaging sharing specialist’s presence in the US. The start-up is focused on developing tools that integrate with EHRs in a bid to make it easier for patients to access and understand their medical images and radiology reports.

The tool, which is available for a yearly $49 subscription fee, enables patients to download, share and transport their medical images. This allows access to their images as well as the ability to share images with other physicians and providers, via PocketHealth itself, or via fax or CD.

PocketHealth says it will use the funding to hire more staff and build more clinical partnerships in Canada, its domestic market, and the US.

The Signify View

When it comes to stakeholders in healthcare, patients have, sometimes literally, more skin in the game than anyone else. It is, therefore, a natural assumption that these patients should want to be as involved as possible in their care. This is one of the fundamental tenets upon which PocketHealth’s plans are based.

PocketHealth indulges this sensibility, banking on the belief that patients will want to feel a sense of ownership over their own medical images, that they should want to be able to look at these images themselves, easily share them with whomever they wish, and even scrutinise their radiology reports using the company’s simple language glossary. Should patients seek such accessibility, some of the Canadian company’s tools could prove attractive. The appetite for outpatient imaging is growing, particularly in the US. PocketHealth’s software will make it easy for patients to be more flexible with their care and take advantage of this growing range of providers available to them.

However, there are some limitations to this opportunity. PocketHealth boasts that it already has more than 500 imaging providers across North America signed up to the service, but this is an agreement which will be approached by providers with some reluctance, or at best, caution. While individual outpatient centres may benefit from making it more straightforward to bring customers across from competitors, by the same token, it is also easier to lose them. At least providers don’t necessarily have to pay for this privilege, with the most basic provider plan available at no cost.

Poking a Bear

Another longer-term challenge for PocketHealth is that much of its functionality is already possible, or relatively straightforward to implement via a provider’s existing EHR or medical imaging IT vendor. PocketHealth may be successful in the short term but will need to quickly consolidate its position, lest it risk other larger and more established vendors sensing an opportunity and rushing to fill it. Together the top three image exchange vendors  in the US (Life Image, Ambra and Nuance) represent 86% of the market. Although these are primarily focused on provider-to-provider exchange, if any one of those vendors decided to extend functionality to support patients’ ability to share images, it could immediately reach a significant portion of US patients.

PocketHealth will face other challenges too. While it is no doubt true that increasing patient engagement can, in some circumstances have a positive impact, how universally these benefits can be applied is likely overstated. This is borne out by a study published in the American Journal of Roentgenology of a comparable initiative by imaging IT vendor Visage, which saw radiologists at NYU Langone record video reports in plain English, to keep patients better informed about their health. While anecdotal feedback of the video reports was positive, uptake was, on the whole underwhelming. This was not only true for radiologists, of which just 105 out of 227 included in the study made video reports, but also patients. In total 3,763 video reports were produced during the study period, but of these, just 864 (23%) were viewed.

Patients Care?

One finding highlighted by the study is the fact that, even when more patient-friendly reports are freely given, actual demand among patients is very low. This apparent lack of interest will only be exacerbated when patients are required to pay, as PocketHealth requires if it is to make any money. Further, it undermines the product’s central premise that patients really will value having constant, direct access to their scans.

Even the product’s plain-English glossary should be treated with caution, suggested as such by PocketHealth’s claim that the company worked with doctors to ensure it is “comprehensive and accurate”. The risk is that many of the findings that can be seen on medical images are complex and detailed. Plain English will, in many instances fail to convey this nuance – there is after all a reason specific medical and technical language is required. While not an immediate threat given physicians will still have access to the original reports, it does introduce a degree of vagary that will not sit comfortably with many physicians, allowing patients to understand the subtleties of their diagnoses.

Fundamental Frailty

These are not trivial issues with the product that can be overcome with software updates, or tweaks to UI. These are fundamental barriers that, on the face of it, could limit the long-term feasibility of the product unless significant changes are made, or additional opportunities are identified. As the value of patient data increases, such an opportunity could be in the commoditisation of deidentified patient imaging data, for example.

Without such prospects, on the other hand, PocketHealth’s longevity looks limited. The vendor may well enjoy some short-term success, utilising its novel approach to capitalise on the growing outpatient imaging sector, and riding the Covid 19-induced interest in digitisation and remote technologies. However, longer-term, PocketHealth is at risk of succumbing to one of two outcomes. If the product is very successful in the short term, large EHR and imaging IT vendors will see the opportunity, and quickly look to offer a patient-facing integrated image exchange themselves, either by developing one internally or picking a company in the space, such as PocketHealth.

Alternately, the vendor’s image-sharing capability will be assumed by large EHR and imaging IT vendors, while the ability for patients to view their own images and read their own reports will be underutilised. It may transpire that most patients who want additional detail about their diagnosis, do so through their doctor, and not an app, resulting in a service that isn’t used.

In either scenario, unless PocketHealth does hold an ace up its sleeve in its product pipeline, the long-term opportunity looks slight. The Canadian vendor therefore needs to capitalise on its current momentum and the novelty of its product to capture as many patients and providers as possible. It should seek to invest in establishing its brand and credibility, while getting customers to use its service. This still might not be enough to grant the vendor the long-term market leadership position it seeks, but it should at least help bolster its valuation and make a buyout more likely than a battle. When all things are considered, that’s a commercial, if not necessarily clinical, win.

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Signify Premium Insight: Viz.ai and the Perils of Success

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Medical imaging AI has a new unicorn, after Viz.ai secured $100m in funding at a $1.2bn valuation. The series D round brings Viz.ai’s total funding raised to more than $250m. This figure places it higher than almost all other vendors, with only HeartFlow and Shukun Technology having declared greater funding totals.

In the funding announcement, Viz.ai also revealed that more than 1,000 sites are now using its care coordination platform, which aims to expand on the vendors’ current focus on stroke detection and triage in order to aid and improve decision making in other clinical areas.

The Signify View

Viz.ai has, over the last two years, been on something of a roll. In August 2020 the Centers for Medicare & Medicaid Services (CMS) granted the vendor’s diagnosis and triaging solution (as well as others functionally equivalent) the first New Technology Add-on Payment for artificial intelligence software. This ruling meant that unlike almost all other AI solutions, providers could be reimbursed for the use of Viz’s tool. The vendor followed this up with a $71m series C funding round in March 2021 to finance Viz.ai’s expansion of its Intelligent Care Coordination platform into both other areas of clinical care and into other global markets. This round also propelled the vendor into the $100m club, of vendors with more than $100m of funding. Adding another $100m pushes Viz.ai into even more rarefied air, but also begs the question of what a vendor which has raised $170m in little over twelve months will do next.

This rapid progression suggests that Viz.ai could be heading towards listing publicly, with the vendor utilising its series D funding to pay for expensive product development to target other clinical areas, and sales activities to capitalise on other territories beyond the US. With these initiatives underway the time would be for ripe for Viz.ai to target an IPO and raise the capital it needs to attempt a rise to dominance. However, in reality, this path could prove to be problematic. One need only look at the attempts to list publicly by other large medical imaging AI vendors to see the challenges that could lie in Viz’s future. HeartFlow, example, raised more than $540m, with its latest funding round, a post series E, completed in 2019. In 2021 the vendor then decided to list publicly via a SPAC merger.  However, this plan was ultimately unsuccessful with the vendor abandoning the move earlier this year, citing unfavourable marketing conditions and an inability to properly value the company. More generally review of broader healthcare technology sector in 2022 year to date versus 2021 has seen a collapse of IPO and SPAC deal volume.

The Demands of the Market

A similar fate could befall Viz.ai, with the appetite of VC investors not necessarily translating into demand from public investors. However, there are differences between the two vendors. If Viz.ai decided to go public now it would be doing so with a valuation, that, while significant, is still more than half that given by HeartFlow when it announced its IPO. This could make such a move more manageable, with the more modest valuation more attainable than that of HeartFlow. Alternatively, Viz.ai could follow HeartFlow’s lead and continue to secure funding privately for several years, potentially even ultimately transitioning to private equity ownership, similar to Circle Cardiovascular Imaging. While the potential to raise significant amounts of capital is diminished by such a strategy, in the young and still maturing medical imaging market answering only to a handful of private investors, satisfied with nuanced yet strategic progress, rather than being obliged to deliver headline-grabbing figures to countless unsympathetic shareholders may be preferable.

There are also technological differences between the two vendors that could grant Viz.ai a very different experience to HeartFlow. While the latter remains, in essence, a vendor with a single tool, albeit one which is clinically very valuable, with a strategy to add other tools targeted at cardiovascular disease, the former is a vendor which has already progressed from offering a single algorithm into a vendor with a fleshed-out stroke care coordination platform, expanded beyond neurology. Conversely, HeartFlow is reliant on a single FFR-CT tool, this is an area with, at present, less competition. While there are vendors such as Keya Medical offering similar functionality, they remain focused on the Chinese market. Several young heart health AI developers could begin to offer their own alternatives, but at present HeartFlow is at limited risk from other vendors. In the stroke care space, however, there are numerous competent competitors, some such as RapidAI have already made headway in the US, Viz.ai’s key market, while others are operating and growing in other territories; potentially a significant challenge to Viz.ai’s international ambitions.

Options & Opportunities

This availability of other stroke detection algorithms could also prove a challenge for other reasons. Viz.ai’s image analysis capability is solid and dependable, but not unique. Instead, what presently sets Viz.ai’s Care Coordination Platform apart is the solution’s workflow element. Instead of focusing myopically on the tool’s diagnostic capabilities, the vendor sought to improve stroke diagnostic capabilities into a clinical care pathway, ensuring that the results of the diagnostics could actually benefit doctors, providers and patients. Even if Viz.ai’s stroke detection algorithm could be technically bettered,  its integration may allow it to have a greater impact.

Successful integration also breeds new challenges in terms of regulation and changing care workflows. While not specifically targeted at Viz.ai, the US-FDA’s recent public reminder on April 11th 2022 was a clear warning to vendors of AI-based triage tools for stroke care to be clear on intended use when marketing to providers, while also reminding providers that adoption of CADt must not replace radiologist diagnostic reporting on potential stroke cases regardless of triage prioritisation result.

Viz.ai has gained significant traction by touting its workflow capabilities more so than its AI image analysis capabilities. However, by securing sales based on this workflow integration, Viz.ai leaves itself more open to competitors. These, on the one hand could be other stroke solution providers, RapidAI for example has worked to improve the analytics and prehospital elements of its own product. As Viz.ai boasts of ever-increasing user numbers and ever higher valuations, other vendors, even those outside of stroke imaging could also be attracted to the space. Large healthcare IT vendors could use their scale and breadth of capability to target the market, offering solutions that link stroke care to other departments and either utilising in-house stroke detection algorithms or partner with one of Viz.ai’s competitors.

Electronic Health Record (EHR) vendors are another type of company that could be interested, these vendors are deeply integrated into hospital networks, but could use workflow tools as an opportunity to expand their reach.  There is some precedent for this, with several EHR vendors already offering breast imaging modules for mammography reporting as part of their Radiology Information System (RIS) modules.

With this heritage there is opportunity for the likes of Cerner or Epic, for example, to build on their existent platforms and develop workflow modules, leaving providers to simply partner with a stroke detection algorithm vendor of their choosing. The impetus for such moves will only increase should reimbursement become permanent, rather than the temporary NTAP at present. Although with this reimbursement being renewed for 2022, all signs point to reimbursement for stoke imaging AI becoming permanent. Not only would such a move make the space more lucrative, but including stroke detection within broader tools could make billing and claiming reimbursement more straightforward, attracting provider’s interest.

Suffering from Success

Despite the potential of such prospects, they remain in the longer-term future. For the time being Viz.ai will continue to gain traction. The company now has the necessary funds to enact its short and medium-term strategic objectives, adding significantly to the list of 1,000 sites it caters for and expanding beyond stroke care to take advantage of other lucrative clinical segments. What’s more, through its own activities as well as through partnerships, such as that with medical device company, Medtronic, Viz.ai should increasingly gain a footing in new markets, e.g., Europe, going toe-to-toe with local stroke detection algorithm developers.

How long these good times can continue to roll remains to be seen. Viz.ai has successfully built a useful and commercially viable product, which is enjoying great success in the meeting rooms of VC investment firms. This success, however, could bring more attention to the space and ultimately be responsible for a humbling of the vendor. Viz.ai must acknowledge this threat and act to capitalise on its current momentum and look to make itself indispensable. Now, like HeartFlow before it, Viz.ai will find that more money, really can mean more problems.

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Signify Premium Insight: Medical Imaging AI’s Top Tier – The $100m Club

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As the medical imaging AI market has matured, so too have the vendors within it. One of the signifiers of this change is the increasing number of vendors that have passed the milestone of receiving more than $100m in VC funding. So far nine vendors have crossed this threshold.

This exclusive club, which counts HeartFlow, Shukun Technology, Infervision, Viz.ai, Aidoc, Lunit, DeepWise, Harrison/Annalise (Annalise) and Keya Medical as members, is a diverse group, with considerable differences between its constituents. While China is the best represented country, with Shukun, Infervision, DeepWise and Keya, there are also vendors from a mix of other countries, with the US, South Korea, Australia, and Israel all home to at least one company. The clinical application focus is also varied, with vendors which focus on cardiac, pulmonary, neurology, MSK and screening all covered.

Among this group there are also some striking similarities. Apart from HeartFlow, which secured its latest funding round in 2019, before going public in 2021, all other vendors secured their latest funding in 2021. For the most part these were Series C or Series D rounds, with the exception being Annalise, which precociously enjoyed a $94m, Series B funding round (which far exceeded the Series B round of any of the other vendors). The similarities between these vendors extends beyond the superficial, however, with most of them being able to attribute their success to several shared qualities, detailed below

Having the Right Solution

Uniting all these vendors is the fact that they offer solutions that promise meaningful improvements. Unlike some AI tools which promise to incrementally improve a radiologist’s efficiency or modestly increase a radiologist’s sensitivity, these vendors offer products which make a significant difference to the diagnostic or treatment pathway for a patient. HeartFlow and Keya Medical’s FFR-CT scans, allow CT imaging to be used instead of invasive, and comparatively more risky procedures, and are far more cost-effective for the hospital. Similarly, Viz.ai’s stroke care solution promises to make a meaningful reduction in the time it takes stroke patients to receive treatment, a factor that can be critical when the smallest of differences in time can make the difference between patients surviving or not. The vendor has also focused on developing a care coordination platform, to address the wider stroke workflow, rather than just a limited slice of the process. Annalise has made similar strides with its comprehensive body area solution; instead of identifying one or several individual diagnostic findings, the vendor’s solution can identify around 125 findings (including pathological, anatomical, and technical findings) giving it greater clinical value than many other algorithms.

The market targeted by a vendors’ solution is also important, with the vendors that have been most successful all targeting health conditions that represent serious global health burdens, and often focusing on high volume scan types, or scans which are time-consuming to read. These are the health conditions and scan types that cost providers, payors, and governments the most significant sums. AI that can help solve these health burdens therefore represents a larger opportunity for providers and an enormous potential customer base for vendors, making the potential returns an investor can expect significant.


One of the perennial questions pertaining to advances in medical imaging technology is who is going to foot the bill for its adoption. The AI market is no different. Providers may be attracted to the advantages promised by an AI solution but could be unwilling or unable to fund it from their budgets, without an immediate, guaranteed return on investment. This is what reimbursement offers. A provider can adopt a tool safe in the knowledge that the cost of its use will be covered. AI can also demonstrate a return on investment in other ways. Many vendors, for instance, highlight the cost savings that the use of their tools offer by making providers more efficient. Others emphasise savings downstream in the patient pathway, or in the future when patients require less expensive treatments later. However, these returns are uncertain, particularly given the nascency of the medical imaging AI market. Reimbursement, by contrast, offers a definite figure on which a provider can base its equation between cost and benefit.

There are as yet, only a handful of solutions being reimbursed globally, most notably of which is HeartFlow’s FFR-CT, which is reimbursed in the US, UK, and Japan. More recently, Viz.ai has paved the way for vendors with AI solutions for large vessel occlusions to receive reimbursement in the US, something which Aidoc amongst other vendors has also taken advantage of. The amounts of reimbursement vary, and the figures that providers can expect from the use of the solutions may be revised down, but the fact they are reimbursed makes it more likely for providers to adopt them initially, and investors more inclined to put capital into them. However, even for the tools that do receive reimbursement to date, there is still a gap between the cost of the AI solution and the reimbursement, a cost which the provider inevitably must cover.


One of the barriers that is retarding the adoption of medical imaging AI is its general lack of clinical validation. Without extensive clinical validation studies providers will have little confidence that the tools developers are selling will have a meaningful impact and bring about the benefits they claim. What is more, without clinical validation studies that quantifies a solution’s impact in a specific use case, there is also the possibility that a provider’s expectations will not align entirely with a solution’s promise. Validation, in short, makes it clear what a solution is, and is not, capable of achieving, therefore allowing customers to confidently make purchasing decisions. Essentially, clinical validation proves to a sceptical that a solution works.

Conducting clinical validation studies is expensive and time-consuming, which can be onerous for young start-ups running on a limited budget, but it is a worthy expense. HeartFlow, Lunit, Keya Medical and Annalise have all made significant investments in this regard, and it has helped them secure ever greater amounts of funding. The validation so far has been focused on proving positive medical outcomes and proving that a solution is a robust clinical tool, but increasingly these developers will also be expected to demonstrate the health economic benefits of their products. Positive health economic studies will further encourage a provider to use a solution, safe in the knowledge that it can have a positive quantifiable clinical, as well as economic impact on their imaging department.

Strong Local Base

While AI tools have global potential, the market, at present at least tends to be localised. Providers have tended to be more confident purchasing solutions that have been developed within their own country or region and trained and validated on predominantly local datasets which best represent their patient populations. Vendors also tend to focus on their domestic markets first. These vendors focus on securing regulatory clearance in their domestic markets, while pilot studies are also frequently conducted in a vendor’s domestic market. This also adds to the confidence of local providers, which can be sure that any solutions will work effectively alongside the workflow tools and protocols prevalent in their region.

The vendors listed above, which have been most successful in their ability to raise investor funding, have not tried to fight this regionalisation despite their ambitions as international vendors, but have instead worked to establish a presence in their local market and take advantage of the opportunities they offer. These successful vendors created robust domestic bases. Some are still focused on them, such as Shukun and Deepwise, while others have started to build on those bases with international expansion.

This localisation is one area where Chinese vendors may have an advantage. As highlighted in a past Insight, investor funding is increasingly moving eastwards, favouring Chinese vendors, while the availability of data in China, the way regulation is applied in China, and the sheer scale of the Chinese market mean its vendors harbour plenty of potential. Other vendors have expanded further, however. Aidoc, for example, established itself in Israel, before investing in expansion into the US (including relocation of its headquarters) before building and leveraging partnerships there.


Vendors often have specific products which have been developed to accomplish specific tasks. As highlighted previously there are some vendors that are focused on expanding beyond their original remit and developing care coordination platforms that offer providers greater clinical value and ensure the utility of solutions. Many of the members of the “$100m club” have also sought to supplement their own capability with that of partners. Aidoc, again, is a prime example of this. Its initial focus was upon developing and commercialising its own in-house applications. It had some success at this and was resultantly able to derive revenue from them. Building a platform around these solutions and then partnering with other AI developers allowed the firm to quickly increase the capability it was able to offer providers, without having to sink its own funds into research and development, validation studies, regulatory approval, marketing, and all the other costs associated with launching AI solutions.

Through these partnerships, Aidoc was able to significantly increase its value to providers, while also being able to derive revenue from other AI vendor’s products. In return, these partners can benefit from Aidoc’s credibility and reach, while allowing their products to be easily sold and implemented at providers. Other vendors are using similar, partner-based strategies. Lunit, for example, has solidified partnerships with imaging informatics and modality vendors, including embedding its solution directly onto the scanner, increasingly facilitating the vendor’s reach into the international market. These are, for the most part, mutually beneficial, and importantly cost-effective strategies for growth.

Accepting New Members

Although these vendors, by one metric at least, represent the top of the market, AI development is so rapid, and growth is happening so quickly that these positions are unstable. To maintain their positions at the top table these vendors cannot be static, and instead, need to keep innovating. To maintain its momentum and please its newly found public investors, HeartFlow, for example, must build on its local and burgeoning global success. The vendor has a successful solution, which is increasingly being adopted, but to maintain its growth trajectory will need to start broadening its portfolio, potentially addressing issues along the cardiac care pathway, for example, as some other vendors have done in stroke care. This will become increasingly important as other vendors start to catch up and compete. HeartFlow itself has had this realisation, noting that it must expand beyond a single algorithm to justify its unicorn valuation.

Other companies need to continue proving themselves. Instead of making life easier for vendors, having such prominent levels of investor funding can also increase the pressure that AI developers are under, forcing them to deliver and show that they are deserving of their valuations. Infervision is among the vendors that have had to address this challenge. Although a Chinese vendor, as part of its growth plan it has had to pivot and reposition itself as an international vendor to be seen as a credible competitor and start secure revenue from around the world.

On the other end of the scale, earlier this year in January, MaxQ AI was forced to retreat from medical imaging AI. As detailed in a previous Insight, there were numerous reasons for this failure, but MaxQ was once one of the most promising stroke care developers. Its demise happened very quickly and stemmed, primarily, from an inability to keep up with competitor’s innovation and commercialise its products. That is a fate that could befall any of these vendors should they take their foot off the accelerator pedal, with the fact that some are not yet generating revenue striking a note of caution.

Other vendors, however, are on the rise and will steadily join this club. Funding is increasingly hard to secure, particularly outside of China, but the potential of AI in medical imaging means that another vendor, one which adheres to these common traits, will soon surely make the cut.

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Signify Premium Insight: Just Getting Started? Harrison.ai Raises $100m

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 recent weeks Australian AI developer Harrison.ai joined an exclusive club. After securing AUD129m ($94m) in Series B funding, the outfit has become one of the handful of well-funded medical imaging AI vendors that have raised more than $100m.

The funding, which brings Harrison.ai’s total raised in the last two years to over $120m, was led by returning investor Horizons Ventures and also saw participation from Blackbird Ventures and Skip Capital. These investment firms were also joined by Sonic Healthcare and I-MED Radiology network two Australian providers which have deployed Harrison’s AI offering, lending the round an unusual level of consumer, as well as financial, weight.

Beyond merely investing in the firm, these provider partners will also help Harrison.ai target new areas of healthcare, with the vendor announcing plans to target pathology, among others.

The Signify View

As medical imaging AI success stories go, HeartFlow’s is hard to beat. As discussed in a previous Premium Insight when the heart health developer listed, it set a new financial benchmark. When it first launched on the New York Stock Exchange, the vendor had a pro forma enterprise value of $2.4bn, becoming medical imaging AI’s first unicorn.

Another of medical imaging AI’s financial flyers is Infervision. This Chinese vendor was itself the subject of a Premium Insight when it received $139m in Series D funding in July, bringing its total funding to more than $210m (despite an undisclosed Series C funding round).

Look back a few years however and these vendors’ series B funding rounds pale in comparison to Harrison.ai’s with Infervision securing $47m in 2018, while HeartFlow’s series B was only $20.4m in 2011. Of course, changing markets and changing VC strategies mean that these figures aren’t directly comparable to the nigh-on $100m that Harrison.ai has just secured for itself, but it does indicate the kind of rarefied company that the vendor is joining. It also begs the question of how such a sum has been achieved.

Comprehensive Valuation

There are a number of factors that have gone into establishing its valuation, but at the core is Harrison.ai’s central product, its Annalise.ai diagnostic imaging AI. Key to this product is its comprehensive approach to diagnostic radiology. Most solutions automatically identify a number of findings on an X-ray, but still rely on a radiologist to identify those not covered. AI vendors are addressing these gaps using various methods including partnering with other developers to add additional capability or creating platforms and bundling individual algorithms into suites which address particular clinical requirements.

Annalise.ai instead aims to ‘solve’ a particular scan type (its focus so far has been chest x-ray) and automatically identify all possible findings on any given image. So far, its solution identifies over 125 findings. In doing so it aims to make the selection, deployment and use of AI easier for providers. Further value could also be added to the solution in future as additional workflow tools are included, such as structured reporting, for example.

This approach looks to be effective, with the vendor’s own validation studies, which were published in The Lancet Digital Health in July, showing that radiologists assisted by the tool performed better in the vast majority of cases than those that weren’t assisted. What’s more the model’s AUC was also found to be statistically superior to unassisted radiologists for almost all findings.

Beyond published research, however, real world indications also show the value of the tools, with several providers choosing to use the tools in their own hospitals, including Sonic Healthcare, and I-MED, which have gone on to invest in Harrison’s Series B funding round. The fact that customers have quickly become investors is quite the endorsement.

The company’s ambitions, however, do not stop at chest x-ray, and they are looking to develop comprehensive solutions to other high turn-over scan types. In the long run, the company wants to address most of the high turnover scan types via its potential portfolio of comprehensive AI solutions. Early on, this was viewed as a potentially risky approach, such is the breadth of competition that has homed in on higher-volume scan types like chest X-ray. However, the comprehensive findings approach in a singular offering has allowed Harrison to stand-out from the crowd of aspirant vendors, most of which are offering a singular or a limited number of findings.

Ambitions in Pathology

The performance of Harrison’s radiology AI offering is only half the story, however, with the vendor’s stated ambition in pathology also having an impact on its prospects.

AI applications in pathology do, after all, hold significant potential, but the conditions for this potential to be realised are not yet in place. The most significant challenge is the general under adoption of digital pathology. However, this is starting to change with several factors such as regulation changes in the US, and the turbulence created by Covid-19 highlighting the lack of digitisation in pathology and giving impetus for change.

As these and other catalysts continue to grow in significance, the adoption of digital pathology will increase. As evidenced at RSNA, this is also a trend among imaging IT vendors which will increasingly incorporate pathology into enterprise imaging platforms. Against this backdrop, pathology AI will be able to find a footing.

The quantitative nature of many tasks in pathology as well as the shortage of pathologists (which is even more acute than the shortage of radiologists) means it is an opportune discipline for AI to have a significant impact, especially as the breadth and complexity of pathology diagnostic findings is a multitude higher than in radiology. This could be particularly true for a vendor such as Harrison, which has been especially thorough with its approach to its comprehensive chest X-ray solution. Frankly, singular point applications will have limited traction in pathology.

Cohesive Competence

Harrison.ai is looking to take this cohesive approach further, expanding out of radiology and addressing another slice of the diagnostic workflow. Longer term this digital pathology tool, the chest X-ray tool and potential future tools could all be integrated, leaving solutions that are more complete in both individual areas, but also along the entire workflow. This cohesion could be particularly useful in areas like oncology, as the broader remit of such solutions would see the vendor providing a service rather than a technology solution. This would enable it to prompt purchasing decisions to be made at a more executive level (e.g., C-suite), tapping into a larger budget pool. However, multi-disciplinary convergence in diagnosis is only just gaining traction in care settings, so in the near and mid-term, Harrison should remain focused on serving each individual diagnostic sector to ensure continued success.

The fact that Harrison is also looking to develop its pathology tool alongside recent customer Sonic is also an advantage. Data is obviously one of necessities for vendors looking to develop AI solutions, but, for pathology in particular, this data is scarce. By partnering with Sonic, Harrison will have access to an abundance of clinical data for algorithm training and refinement, as well as a large user base on which to conduct pilot deployments and validation studies. These are all essential for the successful development of a digital pathology AI tool, and having a route to achieve these already in place will give Harrison an edge over some of its competitors.

Looking to develop a pathology solution was also shrewd from a commercial, as well as a clinical, perspective. While increasing numbers of medical imaging AI vendors are securing ever higher funding rounds, pathology vendors have recently tended to fare better as investors have noted that a surge to adoption is pending, with for example Paige securing $100m in a series C round in January, and PathAI netting $165m for series C in July. This disparity is in part a result of the applicability of some solutions to drug discovery, a market which harbours the greatest returns near-term, but also relates to the relative upside of tackling a pathology market that is still heavily analogue and therefore ripe for disruption.

Of Value and of Worth

In receiving $94m in series B funding, Harrison AI has joined a very exclusive group of medical imaging AI vendors funded over $100m. What’s more impressive is that it has achieved this at an earlier stage than any of its peers. The road ahead is long, and the money will be quickly allocated to address its often quite expensive priorities. Continued commercialisation of its chest X-ray solution will be the first order of business; securing US-FDA regulatory approval and selling into and supporting providers will also require significant funds. Looking further ahead, investing in product development for comprehensive solutions that address other high volume scan types will undoubtedly follow. In pathology, Sonic will provide a short-term commercialisation base, but in the more analogue pathology sector, the firm will also have to take on a degree of market education and evangelism, a process that can have a substantial cash-burn rate.

If these priorities can be achieved, and Harrison.ai can begin generating sizable revenues, then the trajectory for future funding rounds and potential listings could be unprecedented. Moreover, the vendor could have a profound influence on the direction of AI. Many of Harrison’s peers are trying to add value in different ways, such partnering to create suites and developing end-to-end solutions that address entire clinical workflows. Harrison.ai offers another way, creating truly comprehensive solutions for specific use cases and then expanding into other adjacent areas. If the vendor is able to achieve commercial success on a par with its funding success, the developer will no doubt sit alongside HeartFlow as a posterchild of the segment. This could be particularly true if the vendor decides to list in the future.

There are, of course, challenges ahead. A lack of standardisation in pathology could make things harder than the DICOM-based world of radiology, while looking to split focus, as well as investment, between different areas, particularly when the vendor is still so young, could prove to be detrimental to both. Doubly so as it begins to compete with more established competition on both fronts.

These are proportionately minor worries, however. Harrison.ai has progressed carefully and methodically and to the pain of its competitive peers, very quickly. Now, bolstered by extra cash, and guided clinically by its customer partners, the precocious vendor is ready to demonstrate that its worth extends far beyond its valuation.



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