About DIAG

The Diagnostic Image Analysis Group (DIAG) at Radboud University Medical Center is an international research group dedicated to advancing medical image analysis through artificial intelligence. Our researchers are embedded in multiple departments of Radboud University Medical Center, including the departments of Medical Imaging, Pathology, Cardiology, and Radiotherapy. Founded in 2010 by Prof. Nico Karssemeijer and Prof. Bram van Ginneken, DIAG builds on decades of pioneering work in computer-aided diagnosis. Today, the group brings together experts in radiology, pathology, computer science, and biomedical engineering to develop AI systems that improve the detection, diagnosis, and treatment of disease.

Our research spans a broad range of medical imaging modalities, from chest radiography and CT to digital pathology and retinal imaging, across various disease areas, including breast, lung, prostate, pancreas, neuro and musculoskeletal analysis. DIAG bridges methodological innovation and clinical application: algorithms developed in our lab often form the foundation of medical imaging products used worldwide. Explore our Research page for more details.

DIAG closely collaborates with industrial and academic partners worldwide. The group is also the driving force behind grand-challenge.org, a global platform that enables collaborative development, validation, and deployment of AI solutions in medical imaging.

Our Mission

Our mission is to make medical image analysis intelligent, interpretable, and clinically impactful. We aim to develop robust AI systems that match and extend human expert performance in diagnostic imaging, enable trustworthy and transparent integration of AI into clinical workflows, accelerate the translation of research innovations into clinical and industrial applications that improve patient outcomes, foster open science and global collaboration through platforms such as grand-challenge.org and our partnerships with academic and industrial stakeholders, and train the next generation of scientists and clinicians at the intersection of AI, imaging, and healthcare.

Ultimately, DIAG strives to bridge the gap between algorithms and clinicians, turning data into insight, and insight into better care for every patient.