RISC Software GmbH utilizes AI and simulation to elevate healthcare to a new level. Our technologies improve image processing, assist in diagnostics, and enable the development of personalized treatment approaches. Through AI-powered solutions, we enhance healthcare quality, optimize processes, and promote knowledge transfer for sustainable and safe medicine. Our research in AI and simulation increases patient safety through optimal diagnosis and surgical preparation and saves lives through early detection of emerging complications.
Virtual patient models allow the simulation of individual treatment courses and the early identification of potential risks. These personalized approaches help to increase safety and efficiency in patient care. Simulations also offer the opportunity to test and optimize treatment options before they are applied in practice.
In collaboration with leading clinics and research institutions, we develop solutions that are scientifically grounded and practically viable. Our technologies have already been successfully deployed in predicting complications, in surgical training platforms, and in image segmentation. The close integration into medical practice ensures that our solutions provide tangible added value.
Learn how our technologies can shape the future of medicine. Contact RISC Software GmbH to shape the healthcare of tomorrow.
The FEMtech research project "Embodied Perceptions" is developing diversity-sensitive concepts to make subjective symptoms, such as pain, documentable and communicable through customizable 3D visualizations.
The research project MEDI-DOK (Medizinische Effizienz durch Digitale Intelligente DOKumentenanalyse) aims to make unstructured text data from intensive care processable by machine.
QuaMeDis is developing an AI-supported solution for automatically checking medication dispensers to improve patient safety in hospitals and care facilities.
The IASON research project aims to fundamentally improve the treatment of aneurysms in the brain using innovative AI-supported simulation and analysis tools.
The HEART project is researching a non-invasive method for monitoring the body's fluid requirements using ECG signals. AI-supported analyses of large amounts of data support precision medicine and offer patients considerable advantages.
The QML4Med research project aims to explore the potential of the promising fusion of quantum computing and machine learning in a medical context.
Automated data analysis, validation, and machine learning model training
