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Co-written by Dr Sanjay Parekh & Bhvita Jani
Earlier this month Australian AI vendor Annalise.ai announced that it had partnered with Fujifilm Australia to offer its CXR Edge solution on the Japanese vendor’s portable and stationary X-ray machines.
The move will enable Fujifilm to distribute versions of Annalise’s comprehensive decision support solutions that are made to be embedded on medical imaging hardware in Australia, as well as in other select markets such as New Zealand, the United Kingdom and India. Two variants of Annalise’s CXR Edge software will be available to healthcare providers. One, CXR Edge Comprehensive, can detect 95 clinical findings and is designed for use in inpatient, outpatient, and emergency settings. The second, Annalise CXR Edge Critical Care detects 35 findings, and is designed to be used in trauma, emergency, and intensive care settings.
The Signify View
Time is the enemy when it comes to many medical conditions. As such there is a great premium placed on devices and technologies which can save clinicians time and allow patients to be treated more quickly. While efficiency is an important consideration for all X-ray exams, it is often most important for those conducted using mobile systems, which are frequently used in emergency rooms and in other trauma settings. In these situations, doctors need to be alerted to time-critical conditions (e.g., pneumothorax) quickly, so embedding an AI solution to identify these conditions directly on the modality makes sense. In doing so, possible bottlenecks stemming from processing delays or network reliability issues are sidestepped, allowing doctors to see the results of the AI seconds after acquisition. What’s more, embedding AI tools on the modality itself also avoids the requirement for any additional IT infrastructure (e.g., dedicated servers), a factor that could be particularly beneficial in some emerging markets such as India in which Fujifilm has a strong presence.
Fujifilm is not the first medical imaging vendor to take this approach, GE Healthcare, for example originally released its Critical Care Suite in 2019 which also embedded medical imaging AI on the device. The key difference however are the tools’ remits. GE’s Clinical Care Suite is focused only a smaller number of findings (endotracheal tube placement, pneumothorax triage, quality assurance tools), while the Fujifilm and Annalise combination addresses up to 95 findings. This could prove far more attractive to providers. Mobile x-ray systems are often not dedicated to one specific department, instead being used for multi-disciplinary purposes amongst different clinical applications. Offering a wider range of solutions could therefore be a competitive advantage. This is especially true given the challenge of embedding multiple solutions from multiple vendors on the same device. It is likely unfeasible to have numerous separate solutions embedded on the modality, so a multipurpose, comprehensive system is arguably a more realistic way for providers to benefit from such a wide range of capability.
However, despite these advantages, AI solutions embedded on the scanner still have some drawbacks comparted to their PACS-deployed siblings. One of the main disadvantages is that they are unlikely to receive upgrades as frequently. Whereas a PACS-based system will usually receive both feature updates and refinements remotely, frequently bringing both new capabilities and improvements to AI solutions, edge AI solutions are more likely to be left running older versions. This is particularly true if the modality doesn’t have cloud connectivity. This could not only leave radiologists relying on outdated software, but also makes performance issues more likely. This is a particular issue given the nascency of AI adoption and the impressive rate of development.
While Fujifilm is initially offering Annalise’s CXR solution in Australia, it also has plans to offer it in other parts of the world, including India. Here, and in other emerging markets, this lack of updates is unlikely to be a significant issue. On the contrary, these are likely to be some of the best opportunities for embedded AI systems. Many emerging markets have, after all, a distinct shortage of radiologists, and increasing volumes of images. In some situations, having even an outdated version of a comprehensive solution embedded onto an X-ray system could flag critical cases and expedite treatment, rather than the images languishing until a radiologist is available to read them, possibly resulting in missed diagnoses. This is particularly true for the critical care version of the software, which, although detecting 35 findings compared to 95 for the comprehensive solution, is focused on detecting time-sensitive conditions that require immediate treatment.
Earlier is Better
Further, the use of embedded AI is also likely to bring financial benefits to providers. Early detection isn’t only good for patients, it will also facilitate the earlier treatment of patients, which in many cases will save a patient having to undergo more serious, and more expensive treatments.
This will be especially true in single-payor health systems where there is a greater incentive to reduce costs, but private markets such as the US could also benefit. Missed radiological findings can be very costly. Not only because of the added care costs, but also because of the costs of litigation. The fact that the embedded AI tools may help to note findings missed or seen at a more progressive stage by a radiologist, therefore have the potential to save providers considerable amounts of money lost in lawsuits.
Despite this potential, how willing providers will be to pay for such capability remains to be seen. This could depend on how Fujifilm chooses to offer embedded AI. The vendor could add the capability at no extra cost as a way to sweeten a deal and encourage a hardware purchase, alternatively, Fujifilm may choose to only offer it on its highest-end systems in a bid to differentiate the systems in its lineup and encourage customers to spring for the more expensive, higher-tiered systems. Additionally, Fujifilm may also consider upselling these edge solutions across its install base, generating a new source of revenue and enticing potential customers to its AI-enabled premium fleet when their contracts are up for renewal.
Taking the Subscription
The Japanese vendor may also explore some more innovative options such as ongoing subscriptions. Such deals could result in more of a partnership between Fujifilm, Annalise.ai and the customer, ensuring that providers have the support they need to maximise the value they are deriving from the systems. This could also benefit the vendors by turning transactional hardware sales into sticky subscription revenues. It would also overcome the issues of upgrades and would ensure that customers continually benefit from the latest version of the software.
Such a move would be particularly valuable at present. Fujifilm, as with other vendors which offer mobile X-ray systems, capitalised on the demand for the modality during the Covid-19 pandemic. As detailed in Signify Research’s General Radiography and Fluoroscopy Equipment report, global revenues derived from mobile digital radiography systems increased to almost $750m in 2020, up from $413m in 2019 as a result of pandemic-induced demand. Fujifilm, like other vendors will look to continue to capitalise on this spike, and will hope to continue to derive revenues from this expanded install base. Offering AI solutions under a subscription could be one tool that helps the vendor achieve it.
However, regardless of which options are utilised, embedding AI on modalities will not be a huge revenue generator in and of itself for either Fujifilm or Annalise. Fujifilm will boast of the capability to snag some extra deals and upsell to some customers in Australia, but the bigger opportunity lies when it is able to sell its products in India and other emerging markets. Fujifilm has some form in these regions, with its products tending to be more affordable than those of the likes of GE Healthcare, Philips and Siemens Healthineers, while it has also shown it is happy to use innovative methods to try to create business in these areas. In this context, offering embedded AI which seamlessly supplements a limited number of radiologists could be very beneficial. This is particularly true given that Fujifilm has adopted a partnership model. This will give the vendor flexibility to offer different tools in different regions (e.g., Fujifilm has already partnered with Lunit in Mexico and across some regions of South America) or abandon partnerships should better alternatives become available elsewhere.
For Annalise on the other hand, its focus remains on its fuller enterprise-based offering. Offering mobile versions of its solution embedded on mobile X-ray systems will help grow its market share and could serve as an introduction to the company for some providers, but, in isolation, such deals will never allow the vendor to live up to its lofty valuation. Instead, it must focus on selling to providers, with any embedded deals merely an additional revenue stream.
Ultimately it is a smart move, but not one that will have a huge market impact. More significantly, it shows how AI can be embedded into solutions, but these best of breed integrations represent only a small part of a market blessed with numerous approaches. Living on the edge remains a choice not a necessity.
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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 Research. To view other recent Premium Insights that are part of the service please click here