Tag Archives: FDA

Signify Premium Insight: AI Vendors Seeking Approval

There are many barriers a medical imaging AI vendor must overcome if it is to be successful. Some of these are more fluid, can be approached in different ways and leave room for creative problem solving. Others, however, are fixed.

Among the most stringent of these challenges is regulatory approval. It is an essential part of any algorithm’s journey from innovation to product, in many ways literally defining the point at which the fruits of a developers’ labour can begin to be seen.

While such approval is essential for algorithm developers, when considered en masse, as Signify Research has done in its Regulatory Product Matrix, which tracks approvals of AI algorithms across the world, broader trends can also be gleaned about the wider medical imaging AI market.

Signify Premium Insight: US-FDA’s Standardised Appeal

Last month the US-FDA added a new rule to the Mammography Quality Standards Act, adding detail on the reporting of breast density in screening programmes.

The new guidance means that, from September 2024, breast screening centres will be required to notify patients about the density of their breasts, a physiological feature which can influence the accuracy of mammography and a risk factor for developing breast cancer. Providers will also have to convey this density information in specific, non-technical language.

The new rules also standardise the reporting of breast density, establishing four categories to identify breast density levels in the mammography report. Such federal guidance has been on the cards for several years, so what can be expected now it is enacted?

Signify Premium Insight: Annalise Hoping to get Comprehensively Ahead

This Insight is part of your subscription to Signify Premium Insights – Medical ImagingThis content is only available to individuals with an active account for this paid-for service and is the copyright of Signify Research. Content cannot be shared or distributed to non-subscribers or other third parties without express written consent from Signify ResearchTo view other recent Premium Insights that are part of the service please click here.

Co-written by Dr. Sanjay Parekh

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

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

The Signify View

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

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

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


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

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

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

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

Regulatory Burden

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

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

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

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

Diverging Details

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

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

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

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

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

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Signify Premium Insight: Headwinds Abound as AI Vendors Seek Seal of Approval

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

Signify Research has recently updated its AI regulatory database, which tracks the number of AI solutions that are cleared for use by the various regulatory bodies across the world. The number of regulatory submissions has continued to grow rapidly from 2021’s report, with vendors across the globe increasingly readying their solutions for hospital sales, and regulators increasingly acquiescing to their submissions. Since 2016, there were a similar number of CE Mark (189 AI algorithms) as there were US-FDA clearances (192); however, the number of NMPA Class III (China) approvals lagged significantly (17), with the first only issued in early 2020.

The number of CE Mark clearances ramped up faster prior to the advent of the Covid-19 pandemic; the total number of clearances in 2019 was equal to the total number between 2016 and 2019 (54). Since then, a further 81 algorithms have received CE Mark to date. US-FDA clearances have almost doubled since the pandemic (121 AI algorithms) compared with those approved between 2016 and the start of the pandemic (71).

Despite this growth however, most solutions approved continued to cater to familiar sub-specialties, such as chest imaging (e.g., chest x-ray, chest CT) and neuroradiology (e.g., stroke, brain MR) on well-addressed modalities, including CT, and MR. There were several solutions that received approval, for liver imaging and prostate imaging, for example, but these use cases represented only a tiny minority. This could change as drugs to target conditions such as liver cirrhosis or fatty liver disease become increasingly available and make AI tools that can identify and track patients with those conditions more valuable, but at present the demand for such solutions is limited.

Mirroring this is the renewed interest in brain MRI because of new drugs for Alzheimer’s disease being released, with drugs for Parkinson’s disease and multiple sclerosis on the horizon. AI tools that quantitatively analyse brain volume and structure will become even more valuable, especially as many have evolved from classical machine learning to deep learning tools.

A further factor in the limited approval of these solutions is that in some instances imaging is not the first line diagnostic procedure. For these conditions, such as prostate cancer, a diagnostic imaging AI solution fits less easily into existent diagnosis and care pathways, so there is, for the time being, less demand for such tools. Another, simpler reason for the continued focus on the same use cases is that there is growing demand in the market, with existing solutions acting as predicate devices, a factor which can expedite regulatory approval.

AI solutions for MSK imaging also remain relatively scarce compared to other imaging sub-specialties, despite the tremendous potential they may deliver to clinicians. For example, AI solutions will offer significant benefits for speeding up painstaking processes such as segmentation of spinal imaging, or by quantifying conditions such as knee osteoarthritis, supporting clinicians to make more accurate diagnoses, faster, and ultimately improving patient management.

Delayed Diagnosis

There is, however, nuance to the regulatory process. In the US, for example many vendors are tending towards securing approval for triage solutions rather than diagnostic solutions, with the newer triage (CADt) regulatory pathway more straightforward than the traditional detection (CADe) or quantification route. This has somewhat opened up the market, enabling more tools to be approved than would otherwise be possible.

However, in some instances these CADt tools are more limited in functionality, resulting in them being less valuable to clinicians and less attractive to providers (due to the lack of diagnostic support for clinicians beyond the worklist). This could result in a relative glut of commercially available solutions, for which there is a distinct lack of demand.

One vendor that has been affected by this dynamic is Annalise AI, which recently announced it had secured US-FDA clearance for the triage and notification of pneumothorax on chest X-ray (CADt). In other markets, however, the vendor has full regulatory clearance for its comprehensive solution, covering the detection and quantification of more than 125 findings. While the CADt clearance does give Annalise visibility in the US, its attractiveness as a viable tool has been severely compromised in the US, threatening to hamper the commercial momentum the vendor has built in Europe and Australia.

This regulatory reticence could stem from several factors. One issue could be the lack of clinical validation studies that have been conducted using US data, a factor which, given the necessity of training datasets to reflect target populations could be a legitimate concern. Another potential cause is more philosophically driven, with some modalities, such as chest X-ray in the case of Annalise AI, being seen to reflect a lower value use case than other, more advanced modalities and their more headline-grabbing uses such as CT (e.g., FFR-CT and stroke detection). The US-FDA is, in effect, tacitly shaping the development of medical imaging AI in the US. What’s more, it is also likely to enable the approval of large numbers of solutions, but, given the limitations, still hinder commercial uptake in the US.

Problems with Paperwork

Regulatory factors could also stymie the adoption of AI in Europe, with the shift from MDD to MDR, and the introduction of UKCA set to curb the growth in clearances in the EU. There are reports of a significant backlog for MDR, with purported delays of 12 to 18 months in securing CE approval. This will steady the flow of approvals for AI solutions in Europe, but leave those that acted early to secure MDR in a strong position to capitalise, bestowing them with what almost amounts to a first-mover advantage. These vendors will be able to capitalise on the growing appetite for AI solutions, while other vendors are forced to await approval. They will also be able to establish themselves at providers and become the provider’s go-to vendor of choice, making it harder for competitors to displace them as their own solutions are cleared.

While those vendors who moved quickly in securing the new MDR approval should have a relatively smooth ride, there are still difficulties to consider. The clearest regulatory obstacle for any vendor that wishes to trade in the UK from July 2023 is the new UKCA approval. Introduced after the UK left the EU, the new UK Conformity Assessment prevents vendors with European approval selling into the UK, until they have been specifically cleared for the British market. Similarly, vendors that have won UKCA approval will be unable to sell in Europe without undertaking a separate clearance.

While this will require more resource from all vendors targeting both the EU and the UK, some vendors who have undertaken testing in the UK could be particularly affected. The availability of data in the UK has made it an attractive location for vendors to conduct pilot studies, but the value of these studies could be diminished in the eyes of the EU, which may prefer pilot sites within European Union countries. This could also impact which vendors are able to tender for contracts. European hospitals may prefer algorithms trained or validated on local patient data, and whether the UK continues to be considered ‘local’ remains to be seen.

These regulatory headwinds point to stagnation in Western Europe. While the region has seen growth over recent years, this is set to plateau, with more challenges for local vendors and less incentive for foreign vendors to try to make their mark on European soil, a factor compounded by the already fragmented nature of European healthcare. Combined, vendors may increasingly avoid the region to focus on more welcoming markets, such as Latin America and the Middle East.

More of the Same

One market that continues to grow is China. Although the country saw significantly fewer approvals (Class III NMPA) than Europe or the US, the overall total of approvals since the start of 2021 almost doubled compared to the previous year. This highlights the potential in the market, although vendors that are not China-native are unable to access this potential, with the country highly focused on nurturing its domestic capability.

Despite the near doubling in approvals over the past year, solutions that have received NMPA Class III clearance have continued to focus on several specific use cases, including FFR-CT, lung nodule detection, pneumonia, and bone fractures. As detailed last year, the reason for the focus on several specific use cases pertains to the availability of datasets for those tools, with approvals likely to be granted for more use cases as datasets become available. Additionally, further approvals could also be granted utilising successful international tools as predicate devices, as is understood to have occurred in the case of Keya Medical’s FFR-CT Class III approval, given that the NMPA doesn’t appear to harbour a relevant dataset.

Interestingly, there have been few approvals for X-ray and none for ultrasound. In a move that has shades of the US-FDA’s focus on advanced modalities, Chinese NMPA appears to also have a focus on MRI and CT. These modalities enjoy greater reimbursement and are more heavily used in diagnosis (whereas X-ray and ultrasound tend to be used more for screening purposes), as such, solutions centred around these modalities promise greater clinical value than many others.

One vendor that does stand out in China is Shanghai United Imaging Intelligence. It is one of the fastest growing modality vendors in China’s medical imaging market, but it has also secured four Class III approvals, giving it more clearances than any other vendor in the region, despite its broad focus. This includes the company receiving the first-ever Class III approval for its ICH stroke solution. This competence highlights the growing opportunity AI presents for United Imaging beyond modality sales, illustrating the vendor’s ambition to become an all-round competitor in the medical imaging market for both hardware and software, in China and beyond.

Regulatory Review?

Combined, these regions overwhelmingly represent the bulk of AI approvals, with the US-FDA and CE Mark at present, far outperforming China. Some tools from outside these regions have also received approval, with limited approvals in South Korea, despite the country’s strong health tech sector. The same is true in Japan, where a less prominent start-up culture and tendency to focus on hardware rather than software has meant that aside from Fujifilm and Canon, which have focused on AI development, there is only one native independent software vendor which has received approval.

More broadly, the regulatory headwinds discussed above suggest that outside of China, the rate of approvals in Europe, or the sophistication of approved tools in the US could begin to slow after several years of acceleration. Innovation will not be held back, but the speed at which solutions can be commercialised in Europe and the US will suffer. While this could help adoption outside these established strongholds, with vendors sensing potential in other markets, it could prove frustrating for vendors looking to capitalise on the growing interest in AI tools. This is particularly true given other contemporary challenges such as the lack of clinical validation, and the often-unclear return on investment AI tools promise (especially given the lack of reimbursement). For AI start-up vendors with shortening funding runways, this must be a concern.

Regulation is one of the few tools governments that want to encourage AI adoption have at their disposal. As such, particularly given the other headwinds, more efforts may be put into streamlining the process. Of course, standards must be upheld, and the safety of tools must be beyond question, but there is no doubt that some facilitation by these bodies to help AI’s enormous potential to be realised sooner would be of great significance.

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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

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.

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