Tag Archives: Lunit

Signify Premium Insight: Annalise Hoping to get Comprehensively Ahead

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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: The Tightrope Upon Which Lunit Must Walk

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Lunit recently announced that it has received preliminary approval for an IPO on the South Korean KOSDAQ index. Following the approval, the Seoul-based AI developer now plans to submit a listing within the first half of the year.

The outfit already has partnerships with several large international imaging vendors, including GE Healthcare, Philips and Fujifilm to incorporate its AI capability into their imaging systems, but Lunit says the money from the funding round will enable it to further develop its AI product range, and expand its global commercial reach. This will entail promoting its products, which include an AI tool for analysing mammograms, an AI solution for analysis of tissue slides for cancer biomarkers, and a comprehensive AI solution for chest X-rays that detects 10 of the most common findings, in more markets across the world.

The Signify View

That Lunit has decided to place its fortunes in the hands of public investors should come as no surprise. As detailed in a recent Premium Insight discussing medical imaging AI vendors with more than $100m of venture funding, Lunit, like many of its successful peers shares some common traits. The vendor has, for instance, taken a very robust approach to product development. Instead of relying solely on one of the available training datasets, which can introduce some ambiguity and aren’t always perfectly labeled, Lunit has chosen to use training data validated against biopsy results. This helps ground the AI tool’s algorithm and minimise the likelihood of errors. A tool is only as good as the data it is fed so supplementing images with other clinical data is a prudent approach.

In a similar vein, the vendor has also been thorough with regard to clinical validation. One of the hurdles stymieing the broader adoption of AI is a lack of robust evidence. To be profitable AI vendors need to prove their solutions are valuable to providers, to warrant hospital budgets and convince policymakers that their tools deserve reimbursement. To do this these vendors must undertake strenuous, detailed clinical validation studies. These are expensive and time consuming to conduct, but they are necessary. This need is compounded as AI vendors look to grow globally, with developers obliged to prove their solutions are as effective on non-local populations. Lunit has been able to meet this requirement, and can point to a wealth of published studies open to scrutiny, as well as regulatory approvals for its Insight CXR and Insight MMG chest X-ray and mammography solutions in the USA, Europe, Japan and South Korea and for SCOPE PD-L1 in Europe.

Available Options

In addition to this technical capability, the vendor has also been commercially savvy. As well as selling its products directly, Lunit has made its products available on several AI marketplaces, allowing providers which use, for example, Sectra’s imaging IT solutions to incorporate its tools through that company’s Amplifier Marketplace.

More significantly, the vendor has also sought to utilise partnerships to target new markets. It has received $26m from liquid biopsy specialist Guardant Health. As well as boosting Lunit’s bank balance, the collaboration will help the AI vendor target the US oncology market with its SCOPE tissue analysis platform. Lunit has also looked to international imaging vendors to grow, with the company inking deals with GE Healthcare, Philips and Fujifilm helping encourage Lunit’s adoption among those vendors’ install bases.

Such endeavours are for naught, if the products themselves are inadequate. Lunit, however, has avoided this trap. While its range of products is small, comprising of three solutions, one for chest X-ray, a second for mammography and a third for pathological tissue analysis, they are focused on valuable areas. Insight CXR, for example is a solution that is on the rising tide of increasingly comprehensive AI tools which, like those from Annalise AI and Oxipit AI seek to provide greater clinical value to doctors than narrow AI solutions which are often more limited. Similarly, Lunit’s SCOPE solution is set to benefit from the growth of digital pathology and the establishment of closer ties between diagnosis and treatment.

A Time for Temerity?

Despite these strengths, however, listing publicly also represents a risk for the vendor. Reports suggest the listing offers Lunit a valuation of around $500m. This is a far cry from the valuations of the likes of HeartFlow and Viz.ai, which saw valuations as high as $2.4bn and $1.2bn respectively in their recent fundraising endeavours, however it is still a significant sum for a vendor that, according to Signify Research’s AI in Medical Imaging report, achieved just over $1 million in revenue and a loss of $16.5 million in 2020. While the IPO will furnish the vendor with ample capital, it also adds considerable pressure. It risks facing many private investors necessitating consistently strong quarterly performances, rather than sympathetic private investors au fait with the medical imaging AI market, which are likely to be understanding of more modest returns in the name of sustainable long-term progress.

There are other hurdles too. The enormous valuations that some medical imaging AI vendors have been able to achieve through the high availability of funding for AI firms over recent years, have, in some instances proved a hindrance to these AI firms when they have looked to list. The likes of HeartFlow and Keya Medical both sought to go public, before being forced to postpone their plans, in part due to the inability of public investors to match these lofty valuations. Furthermore, in many instances, vendors that did go on and list publicly have seen their share prices fall as they were unable to meet the high expectations of their public investors. This has been true of all Lunit’s closest South Korean peers.

Vuno listed at an initial price of KRW32,150 in February 2021, and is now trading at around KRW10,500. DeepNoid shares initially traded at a price of KRW25,200 in August 2021 but now sit languishing at an all time low of KRW11,400. JLK, meanwhile went public in December 2019 with an initial price of KRW8,330, rallied to KRW14,150 in September 2020 but now trades at KRW6,220. The market as a whole has suffered, with the KOSDAQ falling 13% year-to-date, but JLK, DeepNoid and Vuno have all underperformed even relative to this benchmark, falling 20%, 38% and 44% respectively.

A Hard Market

Making headway amidst these underperforming peers will prove difficult. This challenge will also be compounded by market complexities facing its products. While the tools themselves are sound, they are competing in difficult markets. Mammography for instance has well-established incumbent vendors such as Hologic and iCad, as well as a plethora of breast imaging AI start-ups each trying to eke out a share of the market. Similarly, Lunit’s Insight CXR product will not only face stiff competition, but even when used it will be fighting for a small percentage of the limited reimbursement available for chest X-rays. Both could be successful products, but could be slow to return sizable revenues. Lunit SCOPE is likely to be similar. There are considerable opportunities pertaining to digital pathology AI, clinically as well as in other areas such as drug discovery. However, the digital pathology market is so nascent, it is unlikely to significantly contribute to Lunit’s bottom line for several years.

These weaknesses do point to one area in which Lunit should prioritise following its IPO. As well as commercialisation efforts, building sales and support networks in new markets, Lunit must also spend heavily on the development of new tools. The vendor must channel its expertise into targeting new areas that offer high potential returns, whether for better-reimbursed modalities, high-value use cases, or care coordination tools that expand beyond image analysis itself, Lunit needs to supplement its current strength with tools that will be lucrative into the long term.

Don’t Look Down

Ultimately, Lunit’s growth up to this point has positioned it in a difficult spot. While the vendor must move forward, doing so requires overcoming considerable adversity. This is not a problem that is unique to Lunit, but each medical imaging AI vendor that makes it to this pivotal moment, must navigate it in its own way. Balancing the needs of its investors while continuing to invest in product development and market creation, will be tough. The need to create significant revenues and profits to justify its lofty valuation, while not neglecting the robust way it has approached algorithm training and clinical validation, which helped establish its credentials, means walking a tightrope, where one slip can send a share price tumbling.

Lunit will see a future in which the company is successful. Consistently generating revenues, with sustainable growth, a secure user base and an innovative roadmap. The vendor can lead its investors to this future, but its challenge will be in ensuring they don’t lose faith and fall along the way.

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Signify Premium Insight: Autonomous AI Debate Reiterates Importance of Radiologists

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.

“With the medical imaging AI market still maturing, suggestions of autonomous AI may be a leap of faith radiologists are yet unwilling to take.”

Dr Sanjay Parekh

Lithuanian AI developer Oxipit recently announced that it had secured CE Class IIb certification for its ChestLink autonomous AI solution. According to the vendor, the tool assesses chest X-rays and reports on those scans showing no abnormalities without any radiologist involvement. The vendor claims it is the first such autonomous solution, where a diagnostic evaluation will be carried out solely by an AI tool.

The Signify View

Medical imaging AI has long promised to help relieve the burden faced by overworked radiologists in understaffed hospital networks. Many of the solutions currently available are contributing towards this goal, but do so by enhancing a radiologist, enabling them to do more in the same amount of time. They might, for instance, incrementally accelerate a radiologist’s reading of X-rays, or automatically quantify features on an MR scan, allowing exams to be interpreted more swiftly. However, while these use cases are beneficial, they are not transformative, speeding up existent processes rather than replacing them, or at least complementing them with something new. This additional value is what several of the most successful medical imaging AI vendors have, or at least are attempting, to do.

One application for medical imaging AI, which is arguably the most transformative of all, is autonomous solutions. While not designed to replace radiologists, these tools automate some of the more laborious tasks radiologists undertake, effectively promoting radiologists to a supervisory role and allowing them to focus on more complex and demanding tasks. Oxipit claims its ChestLink solution is the first such tool to have received regulatory approval, therefore becoming the first such tool that can be used commercially in Europe.

The tool is designed to scan chest X-rays and rule out healthy patients, automatically sending results for those that display no indicators of pathology. Any image that the tool does not determine as healthy, is flagged as such for radiologist review. The tool’s use is focused on primary care, where, according to Oxipit, as many as 80% of X-rays are of healthy patients. By ruling out this proportion of patients, ChestLink does have the potential to significantly reduce radiologists’ workloads. With, according to Signify Research’s Diagnostic Imaging Procedure Volumes Database, more than 70m chest X-rays undertaken in Western Europe in 2021, any automation could have a dramatic impact.

Market Malaise

However, while the potential advantages of such autonomous technology is evident, the case for the market being ready for it is less clear. The adoption of medical imaging AI is still very much in its infancy, with many of the most successful tools, even those which are used under close physician supervision, augmenting rather than replacing established clinical workflows. Stroke solutions from the likes of Viz.ai and RapidAI, for example, identify stroke patients more quickly so they can be prioritised. However, if the tool fails to detect an occlusion, the patient will not be prioritised, but will instead be left positioned on the worklist as though the tool had not been used at all. The worst case is the de facto pathway.

The stakes are much higher for an autonomous AI solution, however. If Oxipit’s ChestLink suite is wrong, and incorrectly clears a patient as healthy, the results could be severe. Oxipit has made efforts to assure potential customers of its accuracy, highlighting that prior to its approval the tool has been operating in a supervised manner, in multiple pilot locations for more than a year, and in doing so has processed more than half a million real-world chest X-rays. What’s more the vendor boasts of its 99% sensitivity metric and the “zero clinically-relevant” errors made during pilot studies. Outwardly, these claims are impressive, although, like many young AI start-ups, a lack of published clinical validation makes these figures difficult to fully assess.

Regardless, there are other challenges, a specificity rate of 99% implies a false negative rate of 1%, which, on the volume of scans that such a tool could be assessing could be significant. For edge cases and ambiguous scans, radiologists can also use other contextual information which may have a bearing on a diagnosis. AI suites don’t have that luxury, potentially impacting some diagnoses. Oxipit will no doubt ensure there are systems in place to address these shortcomings, but without a human’s reactive, situationally aware cognizance, there is still scope for problems given the range of unprecedented and unforeseen issues that could arise. This could be a particular barrier when it comes to taking responsibility for errors. If ChestLink is wrong and a patient is impacted, who is liable? Presumably either the autonomous solution must offer such a saving that expensive lawsuits can be stomached as an operating cost, or Oxipit must be held liable, otherwise providers would be unlikely to risk such litigation on such a tool.

What is Normal Anyway?

One of the central imperatives in medical imaging AI is the creation of value for radiologists. One of the ways vendors are achieving this is by developing increasingly comprehensive AI tools, which ‘solve’ a modality and body area combination, striving to identify every single finding for a given body area/modality combination. In the future, this could lead to autonomous tools which mimic a radiologist; assessing a scan for any finding and, either reporting that finding, or, if no finding is present, reporting a negative.

In contrast, Oxipit claims its ChestEye solution can identify 75 findings, relating to 90% of potential pathologies, which is technically impressive but still far less thorough than is required for the basis of an autonomous solution. Oxipit’s approach to ensuring that patients with one of these 10% of pathologies, to which ChestLink is essentially blind, are not given a clean bill of health is by effectively making ‘normal’ a positive diagnosis. Images are, in essence, compared to normal images, meaning that anything which does not conform to the trained pattern of normality is considered abnormal, and sent for radiologist review.

This is not necessarily problematic. In practice, what looks ‘normal’ is a lot vaguer than specific findings, but assuming ChestLink can deal with these vagaries, along with the myriad combination of patient demographic, modality manufacturer, inconsistencies in patient positioning etc, it will have value as a rule-out tool. However, this will be its only use, compared to other more sophisticated comprehensive solutions which offer a more nuanced analysis, and in the future could autonomously make positive diagnoses, as well as negative diagnoses based on the absence of findings.

Fighting for Firsts

These are issues that must be considered, both from a fundamental standpoint of whether such a tool can be usefully deployed, and the question of whether providers will choose to deploy it? This latter question may be complicated further by competition in the space. While Oxipit claims its CE Mark Class IIb approval is the first of its kind for such an autonomous solution, in practice, it appears functionally similar to Behold.ai’s Red Dot solution. This tool was awarded Class IIa approval in 2020, an approval Behold CEO Simon Rasalingham claimed was a “first in kind”, with the tool being the “first autonomous AI algorithm to rule out normal chest X-rays”.

Regardless, Oxipit’s attainment of European approval is an achievement. With the vendor last having secured funding in a seed round likely to be looking for more funding soon, especially if it is to avoid losing touch with competitors like Lunit and Annalise.ai, which have raised $137.8m and $117m respectively. Such an achievement, and the publicity it brings will no doubt help in this regard, so the timing is particularly fortuitous.

The ultimate question, however, is whether providers, radiologists and patients will be willing to hand over control to a computer programme. Interim solutions can be implemented; the UK’s Care Quality Commission, for example, insists that all examinations auto-reported by Behold.ai’s tool must be reviewed by a human within 24 hours. Similar stipulations would likely be attached to the use of ChestLink, with the vendor already insisting that the tool’s deployment begins with a phase devoted to retrospective analysis, a second stage in which the AI shadows radiologists, and only then, in the third stage, can autonomous operation begin.

These, and other such solutions to very central challenges are clumsy and inelegant. They are, however, necessary until more sophisticated answers can be found. These fixes rob autonomous AI of some of the potential it holds, mitigating the benefits it can bring providers. Despite this, they mean that the tools can be used in hospitals. This makes Oxipit’s clearance significant, something to be celebrated and a factor that could tip the scales in the fight for VC funding. But, it is also clear that ChestLink isn’t a complete answer to all the questions posed by autonomous AI, that transformative leap remains, for the time being, out of reach.

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Signify Premium Insight: Of Planes and Purple Cows: MaxQ AI’s Failure Under Pressure

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

Earlier this month, Israeli AI vendor MaxQ AI (MaxQ) announced that it was axing its Accipio range of products for the detection and triage of intracranial haemorrhage (ICH) and completely ceasing the development of image analysis-based AI applications.

The company, which was founded in 2013, will continue to exist, but will instead focus on non-image-based algorithms that look to use vast amounts of medical data to identify anomalies that cause poor clinical outcomes or clinical inefficiencies. However, the pivot has resulted in Accipio sales and marketing personnel being laid off with immediate effect

The Signify View

“We have had a history of losses and we may be unable to generate revenues” warned MaxQ in an investor prospectus from 2018 as it set about an ill-fated attempt to list on the Nasdaq Capital Market. This warning was printed in the chapter of the prospectus entitled ‘Risk Factors’, a chapter which, with the benefit of hindsight, is sadly prophetic. Among those identified risks which proved particularly close to the mark were “failure to articulate the perceived benefits of our solution or failure to persuade potential…customers that such benefits justify the additional cost”; “ Failure to generate broad customer acceptance of or interest in our solutions,”; and the “introduction of competitive offerings by other companies”. These factors and others were instrumental in the failure of MaxQ’s Accipio products, with some aspects more important than others.

Perhaps the most significant of MaxQ’s weaknesses was the Accipio range itself. When the company launched in 2013 as MedyMatch its vision of a product, which was focused on detecting an intracerebral hemorrhage (ICH), was at the forefront of medical imaging AI. In 2022, however, solutions are much more mature. Products from other vendors offering stroke imaging solutions such as RapidAI and Viz.ai address both ICH and large vascular occlusion (LVO), but also add value along the clinical pathway. Instead of focusing solely on detection, these more sophisticated solutions (care coordination platforms as previously described by Signify Research) add other functionality such as triage capability, perfusion quantification, mobile viewer and prehospital workflow applications, and secure care coordination tools. In comparison, other tools from MaxQ never made it to market. There were additional tools in development, but the vendor has been commercially reliant on its Accipio Ix and Ax tools focused only on identification and prioritisation, and slice level annotation and prioritisation respectively. The company had also struggled to obtain US-FDA clearance, a necessity to gaining a foothold in the US, a market dominated by RapidAI and Viz.ai.

Ultimately, for AI solutions to be attractive to providers they must offer them greater clinical value than is offered by the narrow Accipio tools. There are some use cases where narrow AI tools do make sense, such as FFR-CT, but more frequently AI developers need to add additional capability along or across the workflow to make solutions worthy of a provider’s spend. With such competition in the stroke detection market, it was inevitable that those with the weakest value propositions would, sooner or later, falter.

An Appropriate Model?

Another challenging factor contributing to MaxQ’s retreat was its business model, which was highly reliant on channel partnerships.

In some cases, there are advantages of a sales strategy centred around these partnerships. Such setups, for example, can allow vendors to scale very rapidly as they are tapping into an existent customer base. They can also help to establish a young vendor’s reputation, with a partnership from a long-established and well-trusted vendor bestowing credibility upon an unknown developer. However, there is a price to pay for these benefits, with a vendor being dependent on an external sales team. Radiology AI, as a very young market, hasn’t yet become a priority for the vendors charged with selling MaxQ’s software, especially if it risked delaying the sale of a modality scanner or imaging IT software. As such, those vendors’ sales teams would also be unlikely to prioritise the software and promote it as effectively as a direct sales team might.

Another challenge comes in the form of market education. This remains one of the barriers for the medical imaging AI market for AI vendors themselves, let alone a channel partner attempting to convince a potential customer. It is hard to convince providers to allocate budget on any new and untested technology, but this persuasion is made considerably more difficult if a sales team doesn’t have a complete understanding of the product they are promoting. While those vendors selling MaxQ’s products would have an appreciation of the technology, it is unlikely that they would have the same level of nuanced understanding, or the same easy access to additional information as a direct sales team could possess.

Sales Are More Than Transactions

These challenges mean that even under a channel partnership model, an AI developer must still allocate significant resource into the promotion of its products. One example of a vendor that has done this well is Lunit, a vendor who has recently crossed into the ‘$100m club’ of vendors that have secured more than $100m in capital funding. Although it also utilises a channel partnership model, Lunit has also pursued direct sales in its native South Korea, and also invested heavily in clinical validation studies. It has then exploited these studies, to convince sceptical providers of its value. In combination it has also been a steady presence at RSNA and other meetings, and a frequent contributor to expert panels and lecterns at conferences. Even when other partner vendors have sealed transactions, Lunit has been very active in the selling.

For MaxQ this job was made harder still by the limited clinical validation it was able to undertake, which led to the withdrawal of its US-FDA approval for detection. While the product was still approved for use as a prioritisation tool, the lack of FDA approval for its detection capabilities would no doubt have raised doubts in a potential customer’s mind, particularly as other vendors were securing a number of full regulatory approvals, and even in some cases, reimbursement.

MaxQ last secured funding in March 2019 of $30m, at the time a very healthy figure. This however followed the vendor’s aborted attempt to list in 2018, which was set to raise a comparatively small figure of $8m, suggesting an urgent need for cash. This begs the question, if more capital had been raised would MaxQ have been able to overcome the challenges it faced? It would no doubt have helped, but continued investment needs to be earned, and MaxQ, despite its very early entry onto the market, and early de novo FDA approval failed to gain traction. Seth Godin’s Purple Cow marketing theory emphasises the importance of being remarkable (as in the titular bovine) in being noticed. MaxQ AI was remarkable in its earliest days, but as time passed and other more sophisticated solutions were released from other vendors, the Accipio line of products failed to hold interest. MaxQ AI slowly slipped back into the pack.

The Point of Failure

“MaxQ is an aeronautic term that means maximum pressure, which is typically the point where failure occurs”, explained MaxQ AI’s then Chair and CEO, Gene Saragnese in an interview with AiThority in 2019. Sadly, for the Israeli vendor this point of failure has now arrived and, MaxQ AI has become one of the most significant pioneers to falter amidst the consolidatory pressures in the bourgeoning medical imaging AI market. While it is easy for survivors to smugly pore over MaxQ’s mistakes with the benefit of hindsight, many would do well to heed the warnings. There are several vendors that will, in the relatively near future, succumb to similar pressures. One need only look at the competition in some markets to see how challenging things are set to become. In the breast AI market, established leaders are making it increasingly difficult for less established vendors which lack unique products to gain any ground. The chest X-ray AI market, meanwhile has seen some technology leaders with increasingly comprehensive, and increasingly clinically valuable solutions emerge, throwing shade on other, once-promising vendors. Even AI for more advanced imaging, like brain MRI, is becoming increasingly homogenised, with several solutions that lack competitive differentiation appearing at risk of failure.

Consolidation in the radiology AI market is coming. There are simply too many vendors chasing too few dollars for it to be otherwise. Those vendors that will thrive in this consolidation are those that are able to differentiate their products from the competition, add considerable clinical value (beyond feature detection) and solve the pertinent problems that providers face (such as improving workflow efficiencies). Moreover, they must continue to innovate to remain remarkable.

It’s too late for MaxQ AI, but other vendors need to ensure they meet these criteria, lest they become another example left to be dissected.

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