RISC Software GmbH leverages AI and simulation to take healthcare to a new level. Our technologies improve image processing, support diagnosis, and enable the development of personalized therapy approaches. Through AI-powered solutions, we enhance the quality of healthcare, optimize processes, and promote knowledge transfer for sustainable and safe medicine. Our research in AI and simulation increases patient safety through optimal diagnostics and surgery preparation, and saves lives through early detection of potential complications.
Virtual patient models make it possible to simulate individual treatment courses and detect potential risks early on. These personalized approaches help improve 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 sound and practically applicable. Our technologies have already been successfully used for predicting complications, in surgical training platforms, and in image segmentation. The close integration into medical practice ensures that our solutions provide tangible added value.
Discover how our technologies can shape the future of medicine. Contact RISC Software GmbH to help design tomorrow’s healthcare.
The project HEART 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 research project QML4Med aims to explore the potential of the promising fusion of Quantum Computing and Machine Learning in the medical context.
Augmented reality to support preoperative planning
The aim of AIMS is to develop and validate an early warning system using artificial intelligence to warn of deteriorating conditions on the hospital ward before they occur.
The SEE-KID / CEVD research initiative has been working on the computer-aided simulation of eye malpositions and their surgical correction for more than 20 years.
Simulator for training clipping operations
Artificial intelligence for predicting complications in aortic aneurysm treatments.
Inaccurate estimates of burn size can lead to suboptimal medical decisions with significant consequences for patients. With the help of Surface 3D, a precise calculation of the burn size is made possible, thus ensuring patient safety and supporting medical staff in everyday clinical practice.
The research area “Medical Image Processing, Modeling and Simulation based on Artificial Intelligence” (MIMAS.ai) covers a cross-section of highly dynamic research topics, which are becoming increasingly important in medical application fields, not least due to current technological advances.
The State of Upper Austria sees artificial intelligence (AI) as one of the most important technology trends of the next decade and is therefore creating the new Medical Cognitive Computing Center (MC3), a center to research and implement optimal patient care through the use of novel methods in the field of artificial intelligence.
MEDVIS 3D is a software tool for easy and fast reconstruction of aneurysms from medical image data.
One research focus of the Department of Medical Informatics is medical modeling and simulation, which includes in particular research in the field of objective diagnosis and documentation based on virtual patients (three-dimensional models adapted to real patients).
The nARvibrain project (“Augmented Reality supported Functional Brain Mapping for Navigated Surgery Preparation and Education”) aims to improve brain tumor diagnosis and treatment, increase patients’ awareness and understanding of the disease, and enhance the quality of medical education by combining modern Artificial Intelligence (AI) and eXtended Reality (XR) methods.
The ARES project aims to create a software tool that supports rupture risk assessment of cerebral aneurysms by combining clinical, morphological, and hemodynamic metrics to provide an objective overview.
Automatically analyze and validate data and use it to train machine learning models
