Affordable & Responsible AI

Fairness and robustness across populations and resource settings — AI that is affordable, trustworthy and works for everyone.

Most medical AI is built for, and with data from, well-resourced hospitals. We work on affordable and responsible AI: models that are fair and robust across populations, scanners and resource settings; image-quality transfer for portable and ultra-low-field devices; lightweight and explainable models; and guidelines for trustworthy AI in healthcare (FUTURE-AI). Capacity building — summer schools, symposia and the SANAD network — is part of this theme.

Key questions

  • Does a model work equally well for every patient group and every hospital?
  • Can AI close the quality gap between low-cost and high-end imaging devices?
  • How do we grow sustainable AI communities in the Global South?

Funded projects 21 filter all →

Publications 56 filter all →

Exploring the limit of image resolution for human expert classification of vascular ultrasound images in giant cell arteritis and healthy subjects: the GCA-US-AI project

Claus-Jürgen Bauer, Stavros Chrysidis, Christian Dejaco, Matthew J. Koster, Minna J. Kohler, Sara M. Monti, Wolfgang Schmidt, Chetan B. Mukhtyar, Pantelis Karakostas, Marcin Milchert, Cristina Ponte, Christina Duftner, Eugenio de Miguel, Alojzija Hočevar, Annamaria Iagnocco, Lene Terslev, Uffe Møller Døhn, Berit Dalsgaard Nielsen, Aaron Juche, Luca Seitz, Kresten Krarup Keller, Rositsa Valerieva Karalilova, Thomas D. Daikeler, Sarah Louise Mackie, Karina D. Torralba, Kornelis S. M. van der Geest, Dennis Boumans, Philipp Bösch, Alessandro Tomelleri, Markus Aschwanden, Tanaz A. Kermani, Andreas P. Diamantopoulos, Ulrich Fredberg, Nevsun İnanç, Simon Michael Petzinna, Shadi Albarqouni, Charlotte Behning, Valentin Schäfer

Annals of the rheumatic diseases, 2025

All 56 publications →

Datasets 5

Demos 1

Code 4

Teaching 6

In the media 4

Community & events 35