Tag Archives: Regulation

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: AI Making the Move to Maturity

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.

Dr Sanjay Parekh, Senior Analyst

The medical imaging AI market is among the most dynamic of all the sectors in medical imaging. Its nascency, its rate of technical development and the application of the technology are combining to create a market that is changing incredibly quickly.

Despite the volatility of the market, senior analyst and author of Signify Research’s AI in Medical Imaging report, Dr Sanjay Parekh, has been able to discern several key trends in the market.

Great Growth

“The market for AI-based image analysis tools for medical imaging is set to reach $1.36bn by 2026,” Parekh states, “up from $402m in 2021.” Much of this revenue is for stand-alone AI tools, but AI-based advanced visualisation (AV) bundled AI tools are also included (accounting for 27% of the total market in 2021).

This represents a CAGR of 27% between 2020 and 2026, highlighting that the AI market is gaining momentum, many of the reasons for which are clear.

“There was for instance a large flurry of regulatory approvals in 2020 and 2021. In the US in 2020 for example, there were more approvals than in 2018 and 2019 combined. There was also the first wave of NMPA Class III approvals in China. With these regulatory approvals vendors can commercialise their tools.

“As well as having more products on the market, there has been continued progress with regards to reimbursement. There is the continued reimbursement for HeartFlow’s FFR-CT solution in the US, the UK and Japan, as well as parts of Europe. Additionally, pockets of China are already reimbursing the use of FFR-CT tools, but national reimbursement is still pending. There has also been a flurry of Category III CPT codes [for which there is no compulsory reimbursement] provisioned for quantitative image analysis tools for ultrasound, MRI and CT, which as well as encouraging the uptake of AI, could lead to reimbursement. While NTAP payments have also been renewed and expanded, such as the recent Optellum clearance, which has defied the norm and will now receive reimbursement for its Virtual Nodule Clinic solution for lung cancer despite the CPT code remaining as Category III.

“All of these factors combined will help the markets continue to grow.”

Areas of Interest

Growth will not be equal across all clinical segments, however, with four areas, which currently represent around 87% of the market, set to continue to stand out. These are cardiology, neurology, pulmonology, and breast imaging, with each having facets that mean they are likely to continue powering growth for AI vendors.

Use of AI is the most mature in the breast imaging market; however, opportunities for growth are more limited than elsewhere. “Because of the relatively limited number of use cases; namely breast nodule detections and breast density analysis, the breast imaging market will not to be as large as the other three,” continues Parekh.

“Cardiology is likely to account for the largest proportion. This will be driven by two factors. The first is increasing uptake and continued reimbursement for FFR-CT tools. Even accounting for its failed SPAC merger, HeartFlow, one of the success stories for the medical imaging AI market, has a relatively large install base and strong commercial traction as well as still offering an appealing value proposition. There are also opportunities for FFR-CT, especially in China, as vendors like Keya Medical, Shukun Technologies, and Raysight receiving regulatory approval for their FFR-CT tools.

“In addition, clinical guidelines recommending CT imaging as a first line diagnostic procedure will drive the adoption of AI.”

Stroke care is also set to rally.

Neurology will be a growth area mainly because of stroke imaging AI solutions. The NTAP code for stroke LVO, which was first issued in 2020 to Viz.ai and then renewed in 2021, was renewed again in 2022 and it looks set to be made permanent soon, thanks to the uptake of stroke imaging AI tools and the increased use of the code in such instances.

“Not only has the payment been created, but providers are using it and its use shows that providers value the end-to-end stroke solutions which benefit the entire care pathway as well as the radiologist.”

There are also other opportunities within neurology, with brain quantification tools, for example achieving moderate success. Some vendors offering such tools are generating revenue, but, while these will continue to be valued, other drivers such as the commercialisation of drugs for neurodegenerative disease are needed before they become a major driver of growth.

“Finally, in pulmonology, the relative value of using AI market is smaller compared to FFR-CT or head CT for example. Although there are vendors working on comprehensive solutions for both chest X-ray and chest CT that do restore that value, the most successful among them are setting a benchmark for other tools looking to gain traction. Further, the continued roll-out of screening programmes for lung cancer and TB, for example, will drive further traction in this market.”

Relinquishing a Point

There are commonalities across these clinical areas, however. It is becoming clear that the utility of point solutions across modalities and clinical areas is in general, very limited. Developers who can only offer single point solutions are looking increasingly unlikely to be selected by providers.

Instead, tools that offer the most value to providers will gain success. This value, however, can manifest in various ways. Many solutions focus on efficiency, but there are also solutions that could actually slow diagnoses, but still enhance the quality of a diagnosis by offering additional metrics, for example. This value is, in some cases, also no longer derived from incremental improvements in specificity or sensitivity that new tools might offer.

“If you offer 93% accuracy compared to 92%, is that going to make a difference,” Parekh asks. “Are you going to get a better diagnosis or is the patient going to be on a completely different treatment pathway? No. Instead value is extended beyond the analysis of the pixels in an image, to patient care and improvements to the clinical care pathway. The vendors that have started doing that are the ones that are going to succeed.

“Breast imaging tools, for example, that combine detection, quantification and classification of nodules, which are far more valuable than those which only offer nodule detection. Moreover, adding in breast density analysis will enhance the value proposition of such a solution even further. More significantly, however, are the tools that are looking at radiology more broadly and seen to offer value across the clinical care pathway (beyond the radiologist). These solutions can come from vendors which solely offer AI, or those which also offer capability to deploy and integrate AI.

“These vendors can bring in advanced visualisation capabilities, workflow capabilities and even structured reporting capabilities to address a given use case, while also offering their own native or third-party AI image analysis capabilities to create entire workflow packages. That is AI demonstrating value.”

Money to Money

Value is also forthcoming in a broader sense. Despite the turbulence in some tech markets and in some corners of the medical imaging market, investment for medical imaging AI vendors is still available, although it is becoming more discerning.

“Investors seem to be more than willing to continue to back vendors that have already shown progress,” opines Parekh, “but we are not seeing many Angel or Series A rounds.”

“Where we are seeing a lot of action is for the later-stage funding rounds, which are increasing in both size and number. This indicates that a set of market leaders are being established, such as the $100m funding club [a term coined by Signify Research including vendors that have received more than $100 million in total in venture capital funding]. Even with this greater investment in established companies we are starting to see evidence of a market shakeout.

“Last year we saw Nanox acquire Zebra Medical Vision, at the start of 2022 we have seen MaxQ-AI closing its radiology business, and Sirona acquiring Nines. RadNet, a large outpatient imaging group in the US also acquired two Dutch-based AI start-ups Aidence and Quantib to add to its portfolio after previously acquiring DeepHealth, and expand its push to deploy AI across screening for some of the most prevalent cancers. There is also some speculation about some other vendors also making pivots after not receiving funding that was expected. We have seen consolidation coming for a long time, but between the investment being focused on the largest vendors, and the difficulties for the smaller vendors, we are starting to see the shakeout take place.”

The impact of this market shakeout will be different in different regions. One area that is more difficult to make predictions for is Europe. Presently, the Western European market is starting to catch up with the US, but this growth is expected to stagnate in May 2024 when the new European Union Medical Device Regulation (EU MDR) is coming into force. There is currently a backlog of 12 to 18 months for vendors to upgrade their CE Mark to the incoming regulation, not to mention the more stringent requirements for this regulation. This raises the possibility of many vendors missing the deadline and therefore being unable to offer their products commercially in the EU.

Approval Ratings

This could have significant impacts, say Parekh.

“It is more likely that the larger vendors, the ones with the funds to pursue the MDR, will be the first to receive it. If you are a smaller vendor, then you may not want to, or be able to go for MDR approval. Ultimately, that will leave those that have MDR certification by May 2024 with an ‘early-to-market’ advantage over those that don’t. It could effectively level the playing field, and serve as a reset button, with only those that have been able to secure the new certification, regardless of past CE Mark approvals. This regulatory backlog is also therefore likely to hold back the market as a whole.

“It could also lead a lack of innovation, with smaller start-ups and research groups shifting their focus from radiology, keen to avoid the additional barriers they must pass, so there could also be a short-term innovation gap. This is another reason we could see more consolidation in the market.”

Despite these challenges the future is still bright for medical imaging AI vendors. The market increased by more than $60 million between 2020 and 2021, and growth is only set to continue. This shows a young market taking the first steps to maturity and a nascent technology making the first moves toward more mainstream adoption.

“Overall,” Parekh concludes, “it is growing, at a steady pace for now but with a big ramp up in the medium term, from 2024 onwards.”

“All signs are positive.”

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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: 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.

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