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Cranfield, UK, 8th July 2024, Co-authored by Amy Thompson and Ellie Baker –
The medical imaging AI market was valued at over $700 million in 2023, an increase of more than $150 million 2022. The market is projected to reach over $2 billion by 2028, with a robust CAGR 21% throughout the forecast period. The market continued to be dominated by four main clinical segments in 2023, namely cardiology, neurology, pulmonology, and breast imaging, accounting for 89% of market share overall (as shown in Figure 1). Market consolidation is expected to intensify in the near term, evidenced by AI vendors acquiring other AI Independent Software Vendors (ISVs), a progression from last year’s trend of imaging modality vendors acquiring AI ISVs. This shift highlights a dichotomy: some AI ISVs have achieved sufficient commercial traction to pursue acquisitions, while others may face declining revenues and dwindling investment, leading to more frequent market exits. This underscores the growing disparity between market leaders and laggards in many clinical segments and will lead to a shift in market dynamics over the coming years.
Figure 1. Medical Imaging Market by Clinical Segment
Who will pay for AI?
This year, Signify Research conducted a new in-depth analysis of the sales channels AI vendors utilise to go-to-market, to identify the most effective strategies for vendors in the short, medium, and long term. Amidst the recent surge in AI popularity, one crucial question that vendors might have overlooked amongst the hype is: who will pay for the use of AI? Ensuring success in this dynamic landscape requires implementing go-to-market strategies which enable vendors to reach wider audiences
Currently most revenues are recognised through direct sales channels. This channel allows vendors to build stronger relationships with providers and offer hands-on support from AI ISVs to meet technical requirements. As the market moves towards early maturity, this strategy has proven relatively effective, though sales through AI platforms have been quite commonplace for early adopters.
Many providers have embraced the “platform” approach, finding it advantageous to trial and purchase AI solutions through a single broker. However, with most providers still early in AI adoption, many are willing to swallow some of the trade-offs in terms of tighter integration and workflows with “marketplace” platforms. However, the full potential of AI platforms has yet to be realised, as providers at this stage typically deploy only 2-5 solutions across their organizations due to a lack of comprehensive medical imaging AI strategies, governance and funding. As providers start to deploy a greater number of solutions longer-term, the influence of “marketplace” platforms is expected to decrease, with rising competition from not only from tighter integration or partnership with imaging IT vendors, but also AI ISVs expanding their own capabilities, providing more comprehensive solutions through peer partnerships or acquiring vendors.
Imaging IT vendors can typically achieve tighter technical integration of AI solutions with PACS and RIS (or newer enterprise imaging solutions), streamlining workflows and offering a more user-friendly experience, minimizing the need for additional clicks and overlaying or embedding tools into existing diagnostic viewers and toolsets. As well, deploying AI throughout enterprise imaging approaches leverages the existing trust between established vendors s and their clients, facilitating the seamless addition of AI capabilities. Therefore, with the dynamic unfolding across geographic markets, understanding which sales channels: imaging IT, AI “marketplace” platforms (or the increasing demand in pharma) will generate the most success for AI ISVs is crucial for their long-term commercial viability.
The road to success
Notably from the latest round of research, the first AI ISV vendors are reporting expected profitability in 2024 and 2025. For many others, this raises the question of how they achieved break-even and if there is a blueprint for success to follow? New reimbursements in the US, such as those for Ultromics for its EchoGo Heart Failure solution and previous reimbursements for Optellum for lung cancer classification, are examples of how payer funding is providing incentives for providers to use AI solutions. Additionally, the recent establishment of the first Category I CPT code for FFR-CT offers an example pathway for vendors with temporary Category III codes to upgrade to Category I, which has compulsory reimbursement attached. Whilst reimbursement is expected to increase outside of the US, for example across Europe, the fragmented nature of the market poses challenges for implementing these initiatives.
Recent studies have begun to highlight the economic advantages of AI, which is expected to drive its future adoption. Although not yet widespread, significant studies released in 2023, such as the MASAI and FISH&CHIPS studies, and a single-centre study in the UK assessing the use of e-stroke, have provided strong evidence for the benefits of AI in real-world settings. These trials, involving large populations of over 80,000 patients, have provided evidence for the effectiveness of a radiologist-plus-AI protocol in breast imaging, a reduction in all-cause mortality for CAD, and estimated cost savings of around £30 million for the UK economy through implementing AI at stroke centres. Furthermore, a return-on-investment calculator, developed by researchers to assess the Bayer Calantic platform, showed a substantial ROI (Return on Investment) at the five-year mark following its implementation. These types of studies underscore the push from payers for greater health-economic evidence. However, vendors must balance competing priorities, as conducting this type of research is both expensive and resource intensive.
Challenges ahead
Nonetheless, adoption of AI solutions remains in its infancy and is inconsistent between providers and geographies. The market has yet to see widespread adoption of medical AI technologies with commercial and care pathways needing improvement to facilitate AI integration and adoption.
For instance, integrating AI solutions within existing PACS and EHR systems can be technically challenging and resource intensive. In many regions, healthcare systems have been in use for over a decade, meaning that AI solutions are not compatible with current hardware. Either hardware upgrades or the development of solutions to align old and new technologies are required to bridge this gap. Furthermore, the standardisation of AI solutions is needed to streamline integration and enhance adoption rates.
Although radiologists’ perception on AI is improving, getting the buy in from the majority remains essential. Vendors should focus on providing education material, dispelling the ‘black box’ perception associated with AI solutions among radiologists. Understanding radiologists’ needs and collaborating with multiple stakeholders during AI deployment are crucial steps vendors should take to maximize adoption rates. Moreover, working with regulatory and governing bodies to update frameworks and guidelines that are currently preventing the market moving forward, for example in the instance of autonomous AI, is required to ensure that the healthcare landscape remains up to date with technological advancements.
Innovate or Exit
The AI market is transitioning from early adopters to the early majority. This shift is accompanied by the advent of operational AI, generative AI, and AI applications beyond radiology, such as clinical trials, precision medicine, and multi-ology approaches. These advancements support broader enterprise strategies and attention from senior stakeholders at payers and providers, enhancing the value proposition of adopting AI in practise. To remain competitive, vendors must continue to innovate, ensuring their solutions stand out and offer a broad range of capabilities. Vendors still relying on point solutions today, and with reimbursement slow to materialise, must reevaluate their strategies to avoid exiting the market and falling further behind industry leaders.
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About The Author
Ellie joined Signify Research in 2023 as part of the Medical Imaging team. She holds a BSc in Biomedical Sciences from the University of Bath and an MSc in Clinical Drug Development from University College London.
About the AI in Healthcare Team
Signify Research’s AI in Healthcare team delivers in-depth market intelligence and insights across a breadth of healthcare technology sectors. Our areas of coverage include medical imaging analysis, clinical IT systems, pharmaceutical and life sciences applications, as well as electronic medical records and broader digital health solutions. Our reports provide a data-centric and global outlook of each market with granular country-level insights. Our research process blends primary data collected from in-depth interviews with healthcare professionals and technology vendors, to provide a balanced and objective view of the market.
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