Fund F Invests in RYVER.AI within a 1.3 million Pre-Seed Round

RYVER.AI is on a Mission to Tackle Bias in Medical AI with Synthetic Data

Lisa-Marie Fassl and Nina Wöss are proud to announce another investment of their Fund F VC, the European venture fund that backs gender-diverse founder teams.

RYVER.AI, is a Munich-based health-tech startup, and has now officially closed a €1.3 million pre-seed round – among those investors is Fund F by Female Founders. Their founding trio from the Technical University of Munich and ETH Zurich seek to solve the data bottleneck in medical imaging AI without compromising patient privacy. To tackle this challenge, the company develops generative AI to create diverse sets of artificial radiology images that can not be distinguished from the real world.

Increasing urgency for ethical AI in healthcare

The two technical co-founders, Kathrin Khadra and Simona Santamaria, have a background in AI research, focusing on its ethical use and development. When exploring the most urgent fields for ethical AI development, they found that medical AI is not performing reliably across all patient demographics or geographic regions. (1, 2)

Various studies have shown that AI-enabled diagnostics in radiology is performing significantly worse for certain demographic and ethnic minorities, leading to structural underdiagnosis of those underrepresented patient groups. This is because datasets to train and test medical AI are heavily biased. (1, 2, 3)

About 80% of FDA-approved medical AI solutions focus on analyzing Radiology images (e.g. XRay, CT, MRI) (4), and they all have the same problem: a lack of diverse data. Companies spend up to 12 months negotiating collaborations with hospitals or pay up to €500 per radiology image for data acquisition and annotation.


RYVER.AI develops generative models that enable medical AI developers to generate diverse sets of synthetic test and training data in minutes and at substantially reduced cost. In addition, the solution protects patient privacy as the synthetic data is not linked directly to any real-world patient. The technology is available to a range of companies, from specialized startups to large medtech and pharmaceutical firms.

Co-founder and CTO Kathrin Khadra explains: “Our generative AI understands the characteristics of radiological images and the subtle differences between patient groups, scanners and pathologies. Based on this understanding, a large number of completely new images can be generated. As the synthetic data is basically fictitious and not directly linked to a real patient, this is one of the most secure approaches of data anonymization. To check both data quality and data protection in detail, we combine complex mathematical methods with the expert opinion of radiologists.”

Funding and Future Plans

The €1.3 million pre-seed investment secured by RYVER.AI was led by Nina Capital, an international venture capital firm specializing in healthcare technology and based in Spain, which had previously backed the startup’s incorporation in 2022. Other notable investors include BayernKapital (Germany) and Fund F (Austria). “A significant portion of the funding will be used to grow the AI Engineering team and finance the computational cost required to build advanced generative models that can ensure the image quality required in healthcare.”, states Simona Santamaria who leads the research at RYVER.AI.

“Most medical imaging data is biased: by definition, it poorly represents underserved communities, new equipment, and rare diseases. When biased medical imaging data is used for AI development, it ultimately impacts AI’s performance in real-world settings: algorithms searching for low-prevalence conditions have significantly lower positive predictive value than higher prevalence conditions, and the AI can “drift” over time,” said Marta Gaia Zanchi, Managing Partner of Nina Capital. “RYVER.AI’s solution to this massive problem is an engine for the creation of synthetic medical imaging data. RYVER.AI’s diverse founding team blends not only the expertise and commitment required to tackle such technical challenges but also a strong North Star guiding them toward its ethical application for the pursuit of more affordable, higher quality medical AI.”  





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