AI in Medical Imaging Round-up – February 2024

Publication Date: 27/02/2024

Written by

Ellie Baker

Cranfield, UK, 27th February 2024, Written by Ellie Baker –

Hello everyone! I’ll be travelling to Vienna in a few hours ahead of #ECR. I’m sure the next few days will be packed with news of product launches, partnerships and technological innovations but in the meantime, here’s my round-up for February so far.

deepc acquired deepc Benelux, expanding deepc’s European presence. deepc will benefit from the addition of Osimis’ platform partnerships, which focused on best-in-class AI vendors, to its expanding platform portfolio as well as the expertise of a company also embedded in the AI platform market. How deepc will now expand beyond its native European market to target the US will be the next question on the vendor’s mind. This is another move towards increasing market consolidation representing the fourth acquisition within the market in the past three months. As certain vendors face decreasing funding, the potential of being acquired presents as a lifeline.
February has witnessed a surge of VC funding activity, with several companies securing funding rounds. CARPL.ai secured $6 million in seed funding, CoLumbo AI secured a €2.2 million investment in a Late Seed funding round, AZmed raised €15 million in Series A funding and DeepLook Medical raised $1.7M. Despite not reaching the scale of funding seen in 2023 by vendors such as RapidAI, Elucid , and Perspectum Ltd, AI vendors are still able to attract investor interest for smaller injections of capital, enabling them to expand operations and enhance product offerings. For AI vendors, demonstrating early success in raising capital during 2024 places them in a favourable position. However, as many companies in the AI space are facing the depletion of their funding reserves, continued news of successful fundraising endeavours is crucial for their survival and competitiveness in the market.
b-rayZ received CE mark approval for its DANAI technology, which aims to improve breast cancer detection, density assessment and quality-controlled breast positioning through its ability to adapt to the local setting and patient population it is being used on. The company states that its DANAI offering, which integrates into the company’s b-box plus breast diagnostic suite, introduces a custom AI framework which can enhance diagnostic accuracy and streamline operational efficiencies. This type of solution is welcomed, however, with wide-spread adoption for image analysis AI yet to be realised and radiologists still in the process of becoming accustomed to its use, it leaves us to question if radiologists are ready for this ‘paradigm shift’.
Research into the real-world effectiveness of AI solutions is gaining traction, with recent studies from Applied Ergonomics and Clinical Radiology standing out. These studies sought to gauge radiologists’ perceptions of AI in practical use and proposed methods for enhancing validation beyond regulatory standards. Such investigations shed light on the nuanced performance of AI in real-world scenarios compared to controlled clinical trials, pinpointing areas for algorithm refinement. Proposals for independent auditing, surpassing regulatory requirements, emerge as crucial. This approach not only addresses safety and efficacy apprehensions but also facilitates early error detection and establishes benchmarks. Enhanced visibility and comprehensive reporting resulting from these measures will bolster radiologists’ confidence in AI, fostering its widespread adoption in clinical practice.
Qure.ai‘s qSpot-TB device has achieved breakthrough device designation from the FDA, bringing Qure.ai‘s total FDA clearances to four, expanding the company’s opportunities to target the US market. While Qure.ai leads the Tuberculosis Screening AI market, questions arise regarding the impact of entering the US market. The company states that TB cases are on the rise in Western societies post-Covid-19 pandemic; however, focusing on regions with higher TB incidence rates may offer greater benefits. Nevertheless, with WHO endorsing the use of AI for identifying and triaging TB in chest X-ray cases as an alternative to radiologists, the TB screening sector presents a promising area for focus with the potential for reimbursement opportunities in this domain high.
Another FDA approval has been granted to Viz.ai for its VIZ ICH plus solution under the 510(k) clearance. This solution is specifically designed for analysing intracranial hyperdensities, lateral ventricles, and midline shift, offering volume measurements of brain bleeds to facilitate timely and informed treatment decisions. With competitors like Brainomix targetting the US market, Viz.ai‘s ability to now both detect and quantify ICH with VIZ ICH plus bolsters its offering. This advancement not only improves the ability to influence patient outcomes but also aids healthcare professionals in making critical decisions.
Quibimserves as a notable example of how medical imaging AI companies can expand their focus to target additional industries, such as life sciences. Their introduction of QP-Insights, a platform for deploying image-based AI to accelerate drug development programs, exemplifies this pivot. For vendors seeking to bridge medical imaging AI solutions with the life sciences sector, there are potential benefits including access to a market that may not be as heavily regulated and the advantages of leveraging the operational scale and distribution resources of a large pharmaceutical partner. However, it’s important for AI ISVs to approach pharmaceutical partnerships with caution, considering the “high risk, high reward” nature of such collaborations. Deploying substantial resources to support new initiatives in this realm can be challenging, and many co-development projects end without yielding tangible commercial offerings.

That’s all for now, but if you happen to be in Vienna over the next few days and want to take the opportunity to connect in person, do drop me a note.

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 Medical Imaging service provides expert market intelligence and detailed insights for several of the leading AI and Imaging IT vendors. Combining primary data collection and in-depth discussions with industry stakeholders, our thorough research approach yields credible quantitative and qualitative analysis, helping our customers make critical business decisions with confidence. Furthermore, our commitment to seeking a plurality of perspectives across the markets we cover guarantees that our insights remain independent and balanced.

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