Summary:

• Envisionit Deep AI, a South African health tech startup, has raised $1.65 million to develop artificial intelligence products that help speed up diagnosis in radiology.
• The startup’s CEO aims to democratise access to diagnostic imaging and combine AI technology with radiology to transform the way radiologists interpret imaging and make diagnoses.
• Envisionit Deep AI’s products, such as Radify AI, can accurately interpret chest X-rays to detect up to 25 pathologies.

Envisionit Deep AI, a health tech startup based in South Africa, has raised $1.65 million four years after launching. The company’s leadership says that the funding will go a long way in taking is artificial intelligence tech to new levels.

“We have this exciting goal to combine revolutionary technology like artificial intelligence with radiology and we want to transform the way radiologists look, interpret imaging and make diagnosis,” the startup’s CEO, Dr. Jaishree Naidoo, said.

What the startup offers is a suite of products that help speed up diagnosis processes in the radiology section of healthcare. One such product is Radify AI, which sees to it that radiologists get accurate results from medical imaging in less time that would be usually required.

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The product is also available for deployment in rural clinic settings as Naidoo says that the goal is to ‘ democratise access to diagnostic imaging’.

With the number of radiologists in African countries to the population showing the challenge of patients getting urgent and quality care, Envisionit Deep AI could become an important player in the health industry.

In the beginning, the startup built models that interpret chest X-rays to detect up to 25 different pathologies that are common in Africa such as tuberculosis, b****t cancer and pneumonia. Then, during the Covid pandemic, it launched a product that could identify the virus-induced pneumonia from chest X-rays in a matter of milliseconds.

Envisionit Deep AI ensures its models use de-identified data drawn from diverse ethnic groups around the world in its training to ensure accuracy. Radiologists also review its diagnosis through a validation tool to ensure that the machine is on the right track.

So far, the company claims to have attended to 37,511 patients, processed 109,698 images, identified 59,169 pathologies, and started 109,614 studies.

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