Signify Premium Insight: The Technologies Making Moves in Medical Imaging AI

Published: September 23, 2021

23rd September 2021 – One of the components of Signify Research’s Machine Learning in Medical Imaging service is the Product Developments & Technology Trends report which considers some of the most impactful currents in medical imaging AI. The trends discussed in the report are set to shape the future of the AI market for the coming years, particularly as the use of machine learning in medical learning is at a pivotal point. While still a young sector, it is growing rapidly, with both technology maturing and use cases and clinical implementations becoming clearer. Medical imaging AI is becoming a higher priority for both providers and vendors alike, which is enabling some AI outfits to strike increasingly large deals and establish leadership positions. Facilitating these changes are several key aspects of medical imaging AI development.

The Transition Away from Point Solutions

In its current nascent state, the medical imaging AI market is characterised by its fragmentation. It is made up of a host of unproven start-ups, which have tended towards developing narrow AI tools which address a specific problem. However, radiologists still need to review an image to identify other potential findings, limiting the benefits offered by point solutions, and we have started to see these narrow AI algorithms evolving to comprehensive solutions, end-to-end solutions, and AI suites. However, there remains a significant market opportunity for narrow AI solutions addressing high value use cases, for example, FFR-CT, breast lesion detection or C-spine fracture detection. Most notable of these exceptions is FFR-CT, where a solitary measurement is all that is required. The specificity of its use is irrelevant given its ability to improve a patient’s care pathway and spare them from an invasive catheterisation procedure.

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