Horizon Europe & European partnerships

Partner with us in Horizon Europe

Trustworthy, privacy-preserving AI for medical imaging across borders: federated learning that works under the European Health Data Space, from university hospitals to low-resource settings.

Planning a consortium for Horizon Europe or a European partnership? We bring privacy-preserving, trustworthy and affordable AI for medical imaging, clinical data access at the University Hospital Bonn, and a broad international network.

Get in touch EU partner sheet (PDF) Partner description for your proposal ↓

Where we fit in EU priorities

European Health Data Space

Federated learning lets hospitals train and validate AI on their data without moving it, matching the secondary use of health data under the EHDS.

Trustworthy AI and the AI Act

We co-authored the FUTURE-AI international guideline (BMJ 2025) and evaluate models for robustness, uncertainty, fairness and bias.

Global health and Global Gateway

Affordable AI for point-of-care and low-resource imaging, with partner networks in the Middle East, North Africa and beyond.

What we bring to a consortium

Federated learning infrastructure & WP leadership

Design and operation of privacy-preserving training and inference across hospitals, including the national Open Medical Inference (OMI) network, where we are scientific lead.

Medical foundation & vision–language models

Foundation models for radiology, pathology and ophthalmology, adapted to new hospitals, devices and populations.

Trustworthy-AI evaluation

Multi-centre validation, robustness, uncertainty, fairness and bias auditing, following the FUTURE-AI guideline we co-authored.

Training & capacity building

Summer and autumn schools, online courses and researcher exchanges (EEDA, SANAD, BILD, SAAI, MISTAI) that build AI skills where they are needed most.

Partnerships and calls we target

ERA4Health ↗Joint transnational calls on AI in clinical research — partner or coordinator for imaging AI.
European Partnership for Personalised Medicine (EP PerMed) ↗AI for patient stratification and prognosis from imaging (cancer, multiple sclerosis, vascular disease).
Transforming Health and Care Systems (THCS) ↗Implementing and evaluating AI and federated infrastructure in hospital workflows.
Innovative Health Initiative (IHI) ↗Public–private projects with industry; builds on our IMI project BigPicture.
Global Health EDCTP3 ↗Affordable, point-of-care imaging AI with partners in Africa and the Middle East.
Horizon Europe Cluster 1 Health, Cancer Mission & GenAI4EUWork-package lead for federated learning and medical foundation models.
Cluster 1 Health ↗EU Mission on Cancer ↗GenAI4EU ↗
COST Actions & MSCA Doctoral NetworksNetworks and doctoral training on federated and affordable medical AI.
COST ↗MSCA Doctoral Networks ↗

Open calls and deadlines: EU Funding & Tenders Portal ↗

Track record in funded projects

All projects and collaborations →

Policy impact

Our work is cited in policy documents of the OECD, the World Health Organization, the European Union and others:

Expert roles

  • Grant Evaluator · European Commission — Horizon Europe
  • Grant Reviewer · German Research Foundation (DFG)
  • Grant Reviewer · Federal Ministry of Research, Technology and Space (BMFTR)
  • Grant Reviewer · German Academic Exchange Service (DAAD)
  • Grant Reviewer · Swiss National Science Foundation (SNSF)
  • Associate Editor · IEEE Transactions on Medical Imaging (since Apr 2026)
  • Associate Editor · ACM Transactions on Intelligent Systems and Technology (TIST) (since Oct 2025)
  • Editorial Board Member · International Journal of Computer Vision (IJCV) (since 2024)
  • Workshop Chair · MICCAI 2026, Strasbourg
  • Technical Program Committee Chair (since 2024) · MICAD 2024 Manchester · 2025 London · 2026 Edinburgh
  • Co-organiser & Session Chair · FLUID workshop @ AAAI 2025, Philadelphia
  • Organizing Committee (Outreach) · MICCAI 2025, Daejeon
  • Challenges Co-Chair · MICCAI 2024, Marrakesh
  • Program Co-Chair · MIDL 2022, Zurich
  • Special Sessions Chair · IEEE ISBI 2022, Kolkata
  • Organising Committee · DART 2020 and DCL 2020 @ MICCAI
  • Organising Committee · DART 2019 and COMPAY 2019 @ MICCAI

Infrastructure and resources

  • Lab GPU server at Bonn: 8× NVIDIA A100 (64 GB), plus access to university and national HPC resources.
  • Open Medical Inference (OMI) network: national infrastructure of the Medical Informatics Initiative (BMFTR) for privacy-preserving AI training and inference across 16 partner sites in 4 MII consortia; our role: scientific lead. Details: albarqouni.github.io/projects/omi.
  • CoMI AI marketplace (internal) with 12 AI services.
  • Clinical imaging (CT, MRI, X-ray, ultrasound) and digital pathology at UKB, including high-resolution whole-slide scanners.
  • Open resources: curated datasets, open-source code and benchmarks published by the lab — datasets, code, github.com/albarqounilab.

Partner description for your proposal

Ready to paste into Part B ("Participants") of a Horizon Europe proposal. The EU partner sheet (PDF) adds key staff, selected publications and projects.

Universitätsklinikum Bonn (University Hospital Bonn, UKB), Germany
Clinic for Diagnostic and Interventional Radiology — Computational Medical Imaging Research (Albarqouni Lab)

The University Hospital Bonn is one of Germany's leading university hospitals, combining patient care, research and teaching with the Faculty of Medicine of the University of Bonn, a German University of Excellence. The Albarqouni Lab at the Clinic for Diagnostic and Interventional Radiology develops trustworthy and affordable AI for medical imaging and translates it with clinical departments across UKB and with an international network of hospitals and universities.

Role in the project (examples):
- Development of medical foundation and vision–language models for radiology, pathology and ophthalmology
- Federated and privacy-preserving learning across hospitals, including multi-centre validation
- Robustness, uncertainty, fairness and bias auditing of AI models (FUTURE-AI guideline co-author)
- Affordable AI for low-resource and point-of-care settings, including portable and ultra-low-field imaging
- Clinical data access and annotation through UKB clinics, and training and capacity building (summer schools, MSc and PhD teaching)

Principal investigator: Prof. Dr. Shadi Albarqouni, Professor of Computational Medical Imaging Research, University Hospital Bonn / University of Bonn — shadi.albarqouni@ukbonn.de

Planning a consortium? Get in touch — we are happy to discuss roles, work packages and joint proposals.