Healthy AI lab

Healthy AI lab

A collaboration between Siemens Healhtineers, UMC Groningen, Radboud UMC, and the University of Twente.

Hanzeplein 1, 9713 GZ Groningen

Geert Grooteplein Zuid 10, 6525 GA Nijmegen

The research at the HEALTHY-AI Lab aims to develop trustworthy, robust artificial intelligence (AI) technology for use in healthcare, with a primary focus on assisting with prostate cancer diagnosis using magnetic resonance imaging (MRI) and exploring the ethical challenges of achieving trustworthy AI. HEALTHY-AI Lab is a collaboration between Radboud UMC, Siemens Healhtineers, UMC Groningen, and the University of Twente.

Recent breakthroughs in Artificial intelligence (AI) allow its use as an assistive technology in healthcare. However, the adoption of AI is stalling partly because of a lack of trust among clinicians and patients. HEALTHY-AI is about developing trustworthy, robust AI, with a primary application to a prostate cancer diagnosis with MRI. Prostate cancer strikes as many as 1 in 9 men. Recent developments in prostate MRI make non-invasive detection feasible. This avoids biopsies and allows for earlier detection at a more curable stage. However, the rapidly increasing workload and high level of expertise are a huge concern. AI can exploit MRI to its full potential to maximize the impact on quality of care, reduce healthcare costs, and reallocate time from routine decision-making to human-centric care.

Sustainable Development Goals

Research projects

Model-driven AI methods for prostate MRI analysis. Aims to develop AI methods for prostate MRI analysis that are repeatable, reproducible, and safe.

AI-assisted clinical pathway analysis. Aims to develop methods that can handle pathway analysis data and provide trustworthy predictions. Pathway

Prostate MRI detection AI – Scientific quality management for repeatability and safety. Aims to provide AI-based solutions for MRI detection in a continuous feedback framework.

AI-assisted MRI surveillance of prostate cancer. Aims to develop AI that can optimally interpret imaging follow-up.

AI-assisted MR acquisition and steering. Aims to develop AI that helps MRI to scale up to widespread clinical adoption.


Henkjan Huisman
Christoph Brune
Derya Yakar

PHD Students

Henkjan Huisman
Christoph Brune
Derya Yakar


Development, testing, and validation will mostly be performed by members of the radiology departments of the Radboud University Medical Center and University Medical Center Groningen. Siemens Healthineers will have the leading role in implementing the final products.


  • Radboud University Medical Center

  • University Medical Center Groningen

  • Siemens Healthineers

  • University of Twente
Radboud UMC
University Medical Center Groningen
Siemens Healthineers
University of Twente

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