Open Consortium for Decentralized Medical Artificial Intelligence

HealthHORIZON-RIAID: 101057091
EC Contribution
€86,918
Consortium Size
13 orgs
Start Year
2023
Summary

ArtifArtificial Intelligence (AI) will revolutionize healthcare as its diagnostic performance approaches that of clinical experts. In particular, in cancer screening, AI helps patients to make better-informed decisions and reduce medical error. However, this requires large datasets whose collection faces severe practical, ethical and legal obstacles. These obstacles can be overcome with swarm learning (SL) where partners jointly train AI models without sharing any data. Yet, access to SL technology is seriously limited because no studies have implemented SL in a true multinational setup, no practically usable implementation of SL is available, researchers & healthcare providers have no experience with setting up SL networks and policymakers are currently unaware of the broader implications of SL. ODELIA will address & solve these issues: ODELIA will build the first open-source software framework for SL, providing an assembly line for the streamlined development of AI solutions. To serve as a blueprint for future SL-based AI systems, ODELIA partners collaborate as a swarm to develop the first clinically useful AI algorithm for the detection of breast cancer in magnetic resonance imaging (MRI). The size of ODELIA's distributed database will exceed all previous studies and ODELIA's AI models will reach expert-level performance for breast cancer screening. Thereby, ODELIA will not only deliver a useful medical application, but prove the clinical benefit of SL in terms of accelerated development, increased performance and robust generalizability to ultimately save thousands of lives of European patients. ODELIA's success will push partners to serve as nuclei for the exponential growth of the SL network and extend SL to a multitude of medical applications. Thus, patients, healthcare providers and citizens in Europe will be provided with a digital infrastructure that enables development of expert-level AI tools on big data without compromising data safety and data privacy.

Consortium (13)

Project Results (18)

Source: CORDIS, the EU research results database.

Publications (12)
meval: A Statistical Toolbox for Fine-Grained Model Performance Analysis
Lecture Notes in Computer Science, Fairness of AI in Medical Imaging· 2025DOI
Dishantkumar Sutariya, Eike Petersen
Nature Communications
Nature Communications· 2025DOI
Jan Clusmann; Dyke Ferber; Isabella C. Wiest; Carolin V. Schneider; Titus J. Brinker; Sebastian Foersch; Daniel Truhn; Jakob Nikolas Kather
Overcoming regulatory barriers to the implementation of AI agents in healthcare
Nature Medicine· 2025DOI
Oscar Freyer; Sanddhya Jayabalan; Jakob N. Kather; Stephen Gilbert
Scientific Reports
Scientific Reports· 2025DOI
Müller-Franzes, Gustav; Khader, Firas; Siepmann, Robert; Han, Tianyu; Kather, Jakob Nikolas; Nebelung, Sven; Truhn, Daniel
Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging
Communications Medicine· 2025DOI
Oliver Lester Saldanha; Jiefu Zhu; Gustav Müller-Franzes; Zunamys I. Carrero; Nicholas R. Payne; Lorena Escudero Sánchez; Paul Christophe Varoutas; Sreenath Kyathanahally; Narmin Ghaffari Laleh; Kevin Pfeiffer; Marta Ligero; Jakob Behner; Kamarul A. Abdullah; Georgios Apostolakos; Chrysafoula Kolofousi; Antri Kleanthous; Michail Kalogeropoulos; Cristina Rossi; Sylwia Nowakowska; Alexandra Athanasiou; Raquel Perez-Lopez; Ritse Mann; Wouter Veldhuis; Julia Camps; Volkmar Schulz; Markus Wenzel; Sergey Morozov; Alexander Ciritsis; Christiane Kuhl; Fiona J. Gilbert; Daniel Truhn; Jakob Nikolas Kather
Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI
European Radiology Experimental· 2024DOI
Gustav Müller-Franzes; Luisa Huck; Maike Bode; Sven Nebelung; Christiane Kuhl; Daniel Truhn; Teresa Lemainque
Large language models could make natural language again the universal interface of healthcare
Nature Medicine· 2024DOI
Jakob Nikolas Kather; Dyke Ferber; Isabella C. Wiest; Stephen Gilbert; Daniel Truhn
Nature Communications
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)· 2024DOI
Soroosh Tayebi Arasteh; Tianyu Han; Mahshad Lotfinia; Christiane Kuhl; Jakob Nikolas Kather; Daniel Truhn; Sven Nebelung
Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging
Communications Medicine· 2024DOI
Soroosh Tayebi Arasteh; Alexander Ziller; Christiane Kuhl; Marcus Makowski; Sven Nebelung; Rickmer Braren; Daniel Rueckert; Daniel Truhn; Georgios Kaissis
Reconstruction of patient-specific confounders in AI-based radiologic image interpretation using generative pretraining
Cell Reports Medicine· 2024DOI
Tianyu Han; Laura Zigutyte; Luisa Huck; Marc Huppertz; Robert Siepmann; Yossi Gandelsman; Christian Blüthgen; Firas Khader; Christiane Kuhl; Sven Nebelung; Jakob Nikolas Kather; Daniel Truhn
Scientific Reports
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)· 2023DOI
Firas Khader; Gustav Müller-Franzes; Soroosh Tayebi Arasteh; Tianyu Han; Christoph Haarburger; Maximilian Schulze-Hagen; Philipp Schad; Sandy Engelhardt; Bettina Baeßler; Sebastian Foersch; Johannes Stegmaier; Christiane Kuhl; Sven Nebelung; Jakob Nikolas Kather; Daniel Truhn
A European Multi-Center Breast Cancer MRI Dataset
arXiv
Gustav Müller-Franzes, Lorena Escudero Sánchez, Nicholas Payne, Alexandra Athanasiou, Michael Kalogeropoulos, Aitor Lopez, Alfredo Miguel Soro Busto, Julia Camps Herrero, Nika Rasoolzadeh, Tianyu Zhang, Ritse Mann, Debora Jutz, Maike Bode, et al
Deliverables (5)
Websites, patent fillings, videos etc.
Documents, reports
Websites, patent fillings, videos etc.
Other Results (1)
Periodic Reporting for period 1 - ODELIA (Open Consortium for Decentralized Medical Artificial Intelligence)