Our goal
We aim to be the best campus for data & AI driven research in health and healthcare
What?
The Radboud Healthy Data Program is a joint, digital transformation program of Radboudumc (Radboud university medical center) and Radboud University which focuses on health-related data-driven research.
Radboud Healthy Data aims to connect and further develop our expertise in data sciences and AI, with the purpose of enhancing the development of a responsible and sustainable digital infrastructure for the entire Radboud campus.
Focus areas in our program are FAIR data stewardship, AI methods, education, ethical/legal/societal considerations, data-related community activities and scientific breakthroughs. We build upon our strong history and ongoing activities with partners in our networks. Through Healthy Data, we will strengthen our data-driven infrastructure and our collaborative AI research at the Radboud campus with the ultimate aim to improve health and healthcare in society.
How?
Our approach is to work through the following technological and methodological work packages that develop tools, expertise and talents to support the Radboud Healthy Data ambitions:
Work package 1: Unlocking data | |
Peter Bram 't Hoen | Center for Molecular & Biomolecular Informatics, Radboudumc |
Henk van den Heuvel | Humanities Lab, Radboud University |
Work package 2: Computing and storage | |
Arnoud van der Maas | Information Management, Radboudumc |
Trudie Benschop | Information Management, Radboud University |
Work package 3: AI methods | |
Marcel van Gerven | Donders AI Department, Radboud University |
Ton Coolen | Donders Centre for Neuroscience, Radboud University |
Geert Litjens | AI Medical Imaging, Radboudumc |
Work package 4: Ethical, legal, and societal aspects | |
Marianne Boenink | Ethics in Healthcare, Radboudumc / Radboud University |
Pieter Wolters | Law Department, Radboud University |
Work package 5: AI education | |
Johan Kwisthout | Donders AI Department, Radboud University |
Johannes Textor | Data Science, Radboud University |
Work package 6: Applications | |
Martijn de Groot | Health Innovation Labs, Radboudumc |
Tom Heskes | Data Science, Radboud University |
Work package 7: Sustainability | |
Joram Sjoerts | Valorisation, Radboudumc |
Gertjan Bögels | Research & Impact, Radboud University |
In addition, we work on larger projects that require expertise from multiple work packages through multidisciplinary teamwork. These projects will yield new tools and expertise that will be offered through the work package experts.
As a first step in the Radboud Healthy Data program, we will make a thorough inventory across our Radboud campus to ensure that we have a good overview of our collective ambitions, expertises, resources and activities. Thus, we define the current state of digital transformation of our campus (“Ist”), the desired future state (“Soll”), the obstacles to get there, and the solutions to overcome these obstacles.
Our team
Why?
In health and healthcare in particular, data has the potential to transform traditional patient-doctor interactions and healthcare delivery, as intelligent use of data and (AI-based) analytics can be used to support the way in which diseases can be prevented or cured.
Our society is changing and we, as citizens and scientists, are changing with it. The digital transformation that began decades ago has reached a stage where we have access to large amounts of data and tools that impact our lives in unprecedented ways.
This digital transformation logically also has an impact on health-related activities at the Radboud campus (referring to Radboud university medical center and Radboud University). Over time, our researchers have collected a huge amount of valuable data on health-related research topics using laboratory methods (like next generation sequencing and mass spectrometry), imaging approaches (like MRI, CT, ultrasound) and functional (pre)clinical studies. In addition, health data is being generated directly by citizens and patients using e-health and m-health technology, using digital (often wearable) devices for real-time monitoring. Finally, there is a wealth of information in unstructured vocal and textual information that can be used for health(care) applications.
Health data is becoming available more easily and quickly, both to people using the applications and to health (care) professionals and researchers. The ability to connect all this data - both in terms of types of data and over longer periods of time, generated by patients as well as healthcare providers and researchers - enables innovative, powerful AI-based analytics that improve the effectiveness of research findings to support health (care) decisions.
To reach this potential, we aim to solve several challenges to enable our researchers and staff to offer innovative healthcare solutions, based on sound AI models fed by reliable datasets embedded in a data-driven profesional infrastructure.