AI is changing the medical imagery industry🏆

You want to save time to work on complex tasks? Automate all the simple ones. Applied to the field of medical radiology, this means training AIs to process the straight-forward and time-consuming medical files. We’re proud to lead Gleamer’s €1,5 million seed round to take medical imaging productivity to the next level thanks to machine learning & image recognition.

Crédit photo : Harlie Raethel

While running the medical clinics network he’d previously launched, Christian was struck by the productivity issues faced by traumatology radiologists. In the past 10 years, daily workload had doubled while the number of professionals has remained relatively stable (in France). Along with Alexis and Nicolas, the team started working on a software that could read x-ray sheets, detect bone fractures, and suggest a pre-written report to the radiologist.


Gleamer is developed for the mass of broken legs and arms. They are easy to spot, the medical report is boring to write, and the act is cheaply reimbursed by social security in most cases.

I already came across a few AI-fueled medical imagery investment opportunities. Each of them tried to tackle complex clinical issues, such as detecting tumors or breast cancer. Gleamer tackle the problem on the opposite way by providing a tool to save time on standard clinical case, leaving precious time for radiologist to focus their expertise on complex cases. This is what I love about Gleamer.

And I’m not the only one to love it, as the round is led by XAnge, with Elaia, Bpifrance and some fellow business angels.


Building a strong, proprietary data set is crucial. There is no qualified data-set on the market today, and any new entrant will have to build his own. As we’ve seen in the facial recognition industry, lage, available data sets are as important for success than a great technology. This is a new territory for machine learning, and the algorithm is still in training phase. Gleamer has built key partnerships with major radiology groups to built its proprietary dataset.

In comparison, Medical Authorities provided the market with open and qualified data set to accelerate on rare and serious diseases (think lung or breast cancer).


At XAnge we love to explore new territories with amazing technology. The team already invested in machine-learning solutions, such as Reelevant for emails, or Dolead for paid keywords. And we are even working on our own ML tools, on a way to optimize our own sourcing, to detect ventures that we’ve already met and who follow interesting patterns. Until then, there’s a lot going on for Gleamer, including a lot of training, a crucial CE Marking milestone, and its opening to the European and US markets.