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.

Reimbursement

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.

Validation

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.

Partnerships

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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