Aidoc, the AI solution for medical imaging analysis, raises $27M Series B

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Aidoc, the Tel Aviv startup using AI to analyse medical scans, has raised $27 million in Series B funding. The round is led by Square Peg Capital and brings total funding to date to $40 million. The company’s previous backers include Israeli VC TLV Partners, Magma Ventures and Emerge.

Offering a solution aimed at radiologists — and already deployed commercially across 100 sites — Aidoc claims to be able to detect high-level visual abnormalities from various types of medical scans. The idea is that by working in tandem with humans, it’s able to flag the most critical and urgent cases where a faster diagnosis and treatment could save lives.

Initially providing support for CT scans, the Israeli startup recently reached its millionth analysed patient scan. It says it is adding support for oncology and X-ray. The oncology solution will automatically and instantly detect, measure and compare tumour size with past scans as soon as the radiologist opens the image. I’m also told that another feature on the upcoming roadmap is support MRI scans.

Meanwhile, Aidoc has grown its team from 5 people to 60 since we last covered the company in early 2017. It has also taken part in a number of published clinical studies in various journals and has had its algorithms cleared by the FDA and achieve European CE marks.

“From the 100 sites we’re already working with, mounting evidence is demonstrating real value to patients,” says Aidoc co-founder and CEO Elad Walach. “We feel a responsibility to get this technology into as many hospitals as possible, as soon as possible”. The company aims to be deployed in 500 hospitals within the next two years.

Walach says that Aidoc is working with the American College of Radiology DSI to continuously monitor the performance of the company’s commercially deployed solution. The aim is to provide public visibility on the real-life clinical impact, which he argues is crucial for the continued adoption of AI technologies within medical practice.

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