Five Radiology Artificial Intelligence Companies That Somebody Should Build and Invest In
By HUGH HARVEY I’ve previously written comprehensively on where to invest in Radiology AI, and how to beat the hype curve precipice the field is entering. For those that haven’t read my previous blog, my one line summary is essentially this: “Choose companies with a narrow focus on clinically valid use cases with large data sets, who are engaged with regulations and haven’t over-hyped themselves …” The problem is… hardly any investment opportunities in Radiology AI like this actually exist, especially in the UK. I thought it’s about time I wrote down my ideas for what I’d actually build (if I had the funding), or what companies I would advise VC’s to invest in (if they existed). Surprisingly, none of the companies actually interpret medical images – I’ll explain why at the end! 1. Radiological Ontology Modelling OK, this one might sound a bit simple and obvious, but it’s actually the most crucial of all Radiology AI efforts. First, I need to explain something about radiology – it’s not just the clinical specialism of interpreting medical images, it’s a skilled process of converting those expert interpretations into text. Radiologists are essentially acting as Fourier Tranforms – converting digital images to analogue words and sentences written in their own ‘radiology’ language. As a radiologist, I’ve learnt to speak ‘radiology’ &ndas...
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