Helmholtz Munich is a research center with the mission to discover personalized medical solutions for environmentally triggered diseases to promote a healthier society in a rapidly changing world. Germany's largest research organization, the Helmholtz Association, launched Helmholtz AI: This dedicated interdisciplinary platform develops and promotes applied Artificial Intelligence (AI) methods for the Association's main research fields (Health, Energy, Earth and Environment, Information, Space, Matter) in collaboration with its external and university partners. Its central unit operates at Helmholtz Munich in Munich. Our mission is to enable research scientists to leverage AI methods optimally. The Munich consultant team is focusing on Health and collaborates with researchers from the whole Association on projects on cancer and infection research, molecular medicine, as well as neurodegenerative and environmental diseases.

An algorithm developed by researchers from Helmholtz Munich, the Technical University of Munich (TUM) and its University Hospital rechts der Isar, the University Hospital Bonn (UKB) and the University of Bonn is able to learn independently across different medical institutions. The key feature is that it is self-learning, meaning it does not require extensive, time-consuming findings or markings by radiologists in the MRI images. This federated algorithm was trained on more than 1,500 MRI scans of healthy study participants from four institutions while maintaining data privacy. The algorithm then was used to analyze more than 500 patient MRI scans to detect diseases such as multiple sclerosis, vascular disease, and various forms of brain tumors that the algorithm had never seen before. This opens up new possibilities for developing efficient AI-based federated algorithms that learn autonomously while protecting privacy.

Diagnosing blood disorders relies on a century-old method of using optical microscopes to analyze and classify samples of bone marrow cells. The method used to look for rare, but diagnostically important, cells is well-established, albeit laborious and time-consuming. Artificial intelligence (AI) has the potential to improve this method. However, training an AI algorithm requires a large amount of high-quality data. Now a team has used a data set of more than 170,000 microscopic images to train neural networks to identify bone marrow cells with high accuracy.