Radboud AI for Health is an ICAI lab. It is a collaboration between Radboud University and Radboudumc aimed at developing innovations based on artificial intelligence that solve clinical problems. The innovations will be applied in Radboudumc and other healthcare institutes. We perform PhD research projects and BSc/MSc research projects. We also offer courses for Radboudumc personnel on the use of AI in healthcare.
Healthcare is seen as one of the areas where AI will have a profound impact. Data is ubiquitous in hospitals, and is available in a many forms: patient records, clinical signs and measurements, genetic information, text reports, and images. This data forms the basis for decisions, the diagnosis of a disease, detection of acute and long-term risks, treatment plans, treatment monitoring, and real-time support for example during surgery and interventions.
Artificial intelligence can support clinical decisions, assist in numerous ways, improve healthcare by finding better ways to extract clinically useful information from data, and automate tasks and thereby reduce costs and keep healthcare affordable. By carrying out projects within hospitals, the vast amounts of data available in healthcare can be unlocked for research, of course always in compliance with privacy regulations and ethical standards, and this makes healthcare an interesting domain for AI researchers.
The Radboud AI for Health Lab received an initial funding from Radboudumc's innovation board and started in September 2019 with 6 Ph.D. research projects and funding to support around 30 Master or Bachelor research projects every year. We aim to organize two courses every year for Radboudumc employees interested in AI.
The lab profits from the AI expertise available within Radboudumc, such as the Diagnostic Image Analysis Group, and elsewhere in Radboud University, in the Institute for Computing and Information Sciences of the Faculty of Science and the Artificial Intelligence Department of the Faculty of Social Sciences. The lab is housed in the Radboudumc Innovation Space, colocated with iLab and REshape and the Radboudumc Technology Center Deep Learning to implement the lab's innovations into the clinic. The initiative is embedded within the national ICAI network and is actively looking to partner with others to grow further in the near future.
The RTC Deep Learning coordinates all activities within AI for Health. More information about the course, PhD projects, MSc student projects and open vacancies can be found on the AI for Health website.