RAIR is focussed on applying a range of AI algorithms, largely provided by third parties, which can be used to generate "secondary" data from the unstructured images held in its data lake. This data can then be linked to other data, to create valuable new knowledge.
The goal of Health Imageomics is to extract useful information about a patient directly from a medical image.
Images are routinely collected in clinicial practice. But some disciplines, like ophthalmology, are especially dependent on imaging.
A medical image provides a vast amount of information, which is less subjective than a doctor's notes. In a world where data is largely missing, wrong or just plain unintelligible, this data is really valuable.
But to date, our ability to collect data has far exceeded our ability to extract biological insight from it.
This is changing rapidy. Advances in machine learning and computational processing power will allow us to analye these vast stores of existing image data; to characterize patterns and associations and to gain novel insights about how function follows form. This will enhance our understanding of pathology and the efficiency of our response to it.
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