AI in medical imaging is not driven by technological determinism. Companies in this field aim to address the global shortage of radiologists.

It is safe to say that in developed countries the radiology departments are understaffed by 1/3. Moreover, the report by World Health Organization states that 2/3 of the world population does not have access to diagnostic medical imaging.

Yes, more than 5 billion people have no access to radiology services. At this day and age.

As a result of aging populations and various screening programmes, the demand for radiology services is growing at a steady annual rate of 5-10%. Yet in most countries more radiologists retire every year than there are new ones to replace them. It takes 8+ years on average to train a radiology specialist. 

In 2019 Oxipit launched our first product – the ChestEye imaging suite. ChestEye produces preliminary chest X-ray reports and can identify 75 pathologies, covering more than 90% of diagnostic scope encountered on a daily basis. The suite helps to improve radiologist productivity as well as increase accuracy in missed secondary findings.

After CE certification and real world clinical deployments, we focused on getting feedback from medical institutions on what they expect from AI in medical imaging and how AI could improve their daily routine.

The idea for healthy patient report automation came from consultations with healthcare institutions. And our institutional partners were involved at every step of the product development process.

Why have we chosen healthy patient report automation as the primary target for autonomy? Stay tuned for the next post.

This is PART 2 of our post series exploring the road to medical imaging autonomy.

PART 1: AI Autonomy in Medical Imaging: Not with a Bang, but with a Whisper